Information providing method

ABSTRACT

An information providing method for providing a user who uses a device with information via a mobile owned by the user, the method generating the information to be provided to the user, in accordance with: lifestyle information obtained by a lifestyle information obtaining unit receiving the lifestyle information from the device used by the user, the lifestyle information being information on a state of operation of the device; and location information obtained by a location information obtaining unit receiving the location information indicating a location to which the user traveled.

TECHNICAL FIELD

The present invention relates to an information providing method whichgenerates and provides useful information, based on collected userinformation.

BACKGROUND ART

In recent years wearable sensors which measure, for example, user'sactivity and heart rate are developed. Patent Literature 1 (PTL)discloses a technique of measuring biometric information of a user fromwhich emotional condition of the user may be derived.

CITATION LIST Patent Literature

[PTL 1] Japanese Unexamined Patent Application Publication No.2012-112853

SUMMARY OF INVENTION Technical Problem

The technique disclosed in PTL 1, however, is not able to deriveinformation irrelevant to the situation of vital functions of the user.Therefore, information that can be provided to the user is limited.

Thus, the present invention provides an information providing methodwhich generates useful information that is not necessarily correlated tothe situation of vital functions of the user based on user informationand provides a user with the useful information via a mobile.

Solution to Problem

An information providing method according to an aspect of the presentinvention is an information providing method for providing a user whouses a device with information via a mobile owned by the user, theinformation providing method generating the information to be providedto the user, in accordance with: lifestyle information obtained by alifestyle information obtaining unit receiving the lifestyle informationfrom the device used by the user, the lifestyle information beinginformation on a state of operation of the device; and locationinformation indicating a location to which the user traveled andobtained by a location information obtaining unit receiving the locationinformation.

Advantageous Effects of Invention

The information providing method according to the present invention canprovide a user with useful information that is not necessarilycorrelated to the situation of vital functions of the user.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view of an information providing system accordingto an embodiment 1.

FIG. 2 is a functional block diagram of devices of the informationproviding system.

FIG. 3 is a flowchart illustrating a home appliance process which isperformed by a home appliance.

FIG. 4 is a flowchart illustrating a wearable sensor process which isperformed by a wearable sensor.

FIG. 5 is a flowchart illustrating a mobile process which is performedby a mobile.

FIG. 6 is a flowchart illustrating an information input terminal processwhich is performed by an information input terminal.

FIG. 7 is a flowchart illustrating a server process which is performedby a server.

FIG. 8 is a diagram showing a GUI for obtaining consent of a user toprovide lifestyle information on a mobile information terminal.

FIG. 9 is a diagram showing a GUI for obtaining selective consent of auser to provide various pieces of lifestyle information on a mobileinformation terminal.

FIG. 10 is a functional block diagram, including a mobile, and a homeappliance and a wearable sensor which cooperate with the mobile.

FIG. 11 is a flowchart illustrating a mobile process which is performedby the mobile.

FIG. 12 is a diagram showing an example screen displaying a pinpointadvertisement on a smartphone.

FIG. 13 is a diagram showing an example screen displaying busy statusesof lavatories at stations on a smartphone.

FIG. 14 is a diagram showing an example screen displaying restaurants ina rank format on a smartphone.

FIG. 15 is a flowchart illustrating a travel route presentation processusing lifestyle information on use of toilet.

FIG. 16 is a flowchart illustrating a travel route presentation processincluding user selection.

FIG. 17 is a diagram showing a screen of a vehicle navigation devicewhere a multi-lavatory route is selectively displayed.

FIG. 18 is a diagram showing the screen of the vehicle navigation devicewhere a shortest route is selectively displayed.

FIG. 19 is a diagram showing time which takes to arrive at destinationand fuel required by route of travel.

FIG. 20 is a flowchart illustrating a travel route presentation processutilizing lifestyle information on use of lighting.

FIG. 21 is a diagram showing a screen of a vehicle navigation devicedisplaying incident information.

FIG. 22 is a diagram showing an example of an in-ear wearable sensor.

FIG. 23 is a diagram showing a configuration of the server.

FIG. 24 is a diagram showing a configuration of the mobile.

DESCRIPTION OF EMBODIMENTS

To provide a user with useful information that is not necessarilycorrelated to the situation of vital functions of the user, based onuser information, an information providing method according to an aspectof the present invention is an information providing method forproviding a user show uses a device with information via a mobile ownedby the user, the information providing method generating the informationto be provided to the user, in accordance with: lifestyle informationobtained by a lifestyle information obtaining unit receiving thelifestyle information from the device used by the user, the lifestyleinformation being information on a state of operation of the device; andlocation information indicating a location to which the user traveledand obtained by a location information obtaining unit receiving thelocation information.

According to this, lifestyle information indicating user operation,which is a material for estimating patterns of user behavior, isobtained from a home appliance used daily by a user, information to beprovided to the user is generated from the lifestyle information andinformation on the location of the user. Thus, useful information(information suitable for the user) can be provided to the user. Forexample, it is impossible to determine the likelihood that the userpurchases canned coffee every morning only from biometric information ofthe user, such as heart rate. However, it can be estimated from usagehistory (lifestyle information) of a coffee maker, i.e., a homeappliance, whether the user habitually drinks coffee every morning. Itcan then be estimated that the user is likely to purchase acoffee-related product or the like if a user, who is estimated tohabitually drink coffee every morning, is without drinking coffee in themorning. Thus, according to the information providing method, usefulinformation can be provided to the user. Moreover, according to theinformation providing method, the lifestyle information is obtained froma home appliance, which thus obviates the needs for requiring a user toinput information indicating patterns of his/her behavior through aninformation terminal or the like.

For example, the location information obtaining unit may successivelyobtain the location information of the user, and the informationproviding method may include: (a) predicting, from the lifestyleinformation obtained by the lifestyle information obtaining unit,behavior of the user at a location derived from a plurality of pieces ofthe location information on the user obtained by the locationinformation obtaining unit; and (b) generating the information to beprovided to the user, based on a result of predicting the behavior ofthe user in step (a).

According to this, user behavior in the future is predicted inaccordance with patterns of user behavior, and the predicted userbehavior and a result of prediction of a location of the user in thefuture are combined. Thus, more useful information can be provided.

Moreover, for example, in step (a), user behavior related to an item orservice that has a given connection with an intended use of the devicemay be predicted, and in step (b), information related to the item orthe service may be generated.

It should be noted that, for example, an intended use, which is anattribute, of a coffee maker (a home appliance) is to provide coffee,and an attribute of an item, i.e., canned coffee or an attribute of aservice, i.e., providing coffee at a cafe is to support demand forcoffee. These attributes can be said to have a given connectiontherebetween.

According to this, the demand can be reasonably forecasted and effectiveinformation can be provided.

Moreover, for example, the lifestyle information obtaining unit mayobtain a plurality of pieces of lifestyle information by receiving theplurality of pieces of lifestyle information from plural devices used byplural users, and step (a) may be predicting behaviors of the pluralusers at locations derived from a plurality of pieces of locationinformation of the plural users from the plurality of pieces oflifestyle information obtained from the plural devices used by theplural users.

According to this, by collecting the lifestyle information of aplurality of users, results of prediction of user behaviors can bereflected to information to be provided. Thus, more useful informationcan be provided.

Moreover, for example, the information providing method may furtherinclude (c) attempting, for each of the plural users, to obtain consentfrom the user to obtain lifestyle information from a device among theplural devices which is used by the user before the lifestyleinformation obtaining unit obtains the lifestyle information.

According to this, user privacy is concerned.

Moreover, for example, in step (b), the information to be provided maybe generated in a manner distinguishing between the plural users to whomthe information is to be provided, so that the information provided to auser from which the consent is obtained in step (c), among the pluralusers, includes more content than the information provided to a userfrom which the consent is not obtained in step (c).

According to this, to collect a great number of pieces of informationand provide more useful information, prompt can be made to the user toobtain consent from the user, and fairness between users can be ensuredto some extent as well.

Moreover, for example, in step (a), the behavior of the user may bepredicted based also on biometric information on the user obtained by abiometric information obtaining unit receiving the biometric informationfrom a device which measures the user.

According to this, accuracy in prediction is enhanced, thereby providingmore useful information.

Moreover, for example, the mobile may detect and transmit a location ofthe mobile, and the location information obtaining unit may obtain thelocation information indicating the location of the user by receivingthe location information from the mobile.

This obviates the needs for the user to separately own a mobile, whichis an information providing medium, and a device for obtaining thelocation information.

Moreover, for example, the location information obtaining unit maysuccessively obtain the location information, and successively performsteps (a) and (b).

According to this, practical and most recent information can be providedin real time.

Moreover, an information providing method according to an aspect of thepresent invention is an information providing method for providingcontrol information to a device included in a mobile owned by a user whouses a device, the information providing method generating the controlinformation to be provided to the device included in the mobile, inaccordance with: lifestyle information obtained by a lifestyleinformation obtaining unit receiving the lifestyle information from thedevice used by the user who owns the mobile, the lifestyle informationbeing information on a state of operation of the device used by theuser; and location information indicating a location to which the usertraveled and obtained by a location information obtaining unit receivingthe location information.

This allows automatic control of a device included in the mobile basedon the lifestyle information and the location information to beachieved.

Moreover, a mobile according to an aspect of the present invention is amobile which provides a user who uses a device with information, themobile including: a lifestyle information obtaining unit configured toobtain lifestyle information by receiving the lifestyle information, thelifestyle information being information on a state of operation of thedevice used by the user; a location information obtaining unitconfigured to obtain location information on the user by detecting alocation to which the user traveled; an information generation unitconfigured to generate the information to be provided to the useraccording to the lifestyle information and the location information; andan information presentation unit configured to present the informationgenerated by the information generation unit to the user.

The mobile, according to the above configuration, can provide usefulinformation derived from patterns of user behavior.

These general and specific aspects includes a combination of one or moreof a device, system, method, integrated circuit, computer program, orcomputer-readable recording medium.

Hereinafter, embodiments according to the present invention will bedescribed with accompanying drawings.

It should be noted that the embodiments described below are merelypreferred illustration of the present invention. Values, shapes,materials, components, disposition or connection between the components,and steps and the order of the steps are merely illustrative, and notintended to limit the present invention. Among components of thefollowing embodiments, components not set forth in the independentclaims indicating the top level concept of the present invention arewill be described as components that can be added arbitrarily. Thefigures are schematic illustration and do not necessarily limit thepresent invention that precisely shown.

In each embodiment, mainly, an information providing method will bedescribed which estimates patterns of user behavior, such as lifesituations, preferences, habits, by collecting information that is basedon user manipulation of a home appliance, predicts user behavior in thefuture, and generates and provides beneficial information in response toa result of the prediction. Here, the prediction of the user behavior inthe future includes, for example, forecasting of demand for an item or aservice, such as action appetite for obtaining a particular item oraction appetite for having the benefit of a particular service.

Embodiment 1

Hereinafter, an embodiment 1 which is an aspect of the present inventionwill be described.

FIG. 1 is a schematic view of an information providing system 100according to the embodiment 1.

The information providing system 100 implements an information providingmethod as follows. Generally, the information providing method is amethod of recording lifestyle information which is information on astate of operation of a home appliance which operates in response touser manipulation, estimating, based on the lifestyle information,patterns of user behavior in using the home appliance and predictinguser behavior in the future, and providing information which isgenerated based on a result of the prediction and location informationof the user. FIG. 1 shows the information providing system 100, withreference to an example, given two home appliance users at two houses.

As shown in FIG. 1, the information providing system 100 includes: homeappliances 101 a and 102 a; mobiles 101 b and 102 b; wearable sensors101 c and 102 c; information input terminals 101 d and 102 d; a network103; and a server 104. The home appliances 101 a and 102 a are installedin respective two houses (a house 101 and a house 102). The mobiles 101b and 102 b are owned by users of the respective appliances 101.a and102 a. The wearable sensors 101 c and 102 c are worn by the users of therespective appliances 101 a and 102 a. The network 103, such as theInternet, enables these devices to be communicable with the server 104.The network 103 is connected to an information presentation device 105.Each device, which transmits information via the network 103, pre-storestherein destination information, such as an IP address of a device totransmit the information to.

FIG. 2 is a functional block diagram of the devices of the informationproviding system 100. In the figure, the home appliance 101 a, themobile 101 b, the wearable sensor 101 c, the information input terminal101 d, and the server 104 are depicted and the other devices areomitted.

Here, the home appliances 101 a and 102 a are devices each of whichincludes a lifestyle information recording unit 11 and a communicationunit 12. The lifestyle information recording unit 11 records lifestyleinformation which is information on a state of the home appliance whenoperates in response to user manipulation. The communication unit 12includes a communication circuit and externally transmits the lifestyleinformation. In other words, the home appliances 101.a and 102 a aredevices which connect to the network 103 via wireless or wiredconnection. For wireless connection, for example, wireless communicationtechnology, such as Bluetooth (registered trademark) and a wireless LAN,is employed. It should be noted that the home appliances 101 a and 102 amay each have functionality of collecting and recording lifestyleinformation of the user from one or more other devices connected to thenetwork 103. Examples of the home appliances 101 a and 102 a includeappliances such as a coffee maker, a refrigerator, a TV, and a lightingfixture, or household equipment such as a toilet, a bath module, awashstand, or a combination thereof.

Examples of the lifestyle information recorded by the home appliance 101a or 102 a include information associated with a time instant, such asmanipulational information, operational status, power consumption, andoutput of various sensors mounted within the home appliance. The homeappliances 101 a and 102 a each pre-store a unique home appliance ID(identification information) into an internal nonvolatile memory or thelike. The communication unit 12 externally transmits the lifestyleinformation having the home appliance ID attached thereto.

The mobiles 101 b and 102 b each include a location measuring unit 41and a communication unit 42. Examples of the mobiles 101 b and 102 binclude mobile terminals, such as smartphones or tablets, which havelocation information acquisition capabilities that utilize locationinformation of GPS or a Wi-Fi (registered trademark) router, a vehiclenavigation device utilizing GPS, or a vehicle having the vehiclenavigation device mounted therein. The mobiles 101 b and 102 b alsoconnect to the network 103 via wireless or wired connection. Forwireless connection, for example, a mobile phone network (3G/LTE) orWi-Fi (registered trademark) is employed. The mobiles 101 b and 102 beach pre-store a unique mobile ID into an internal nonvolatile memory orthe like. The communication unit 42 including a communication circuitexternally transmits the location information having the mobile IDattached thereto.

The wearable sensors 101 c and 102 c each include a biometricinformation measurement unit 21 and a communication unit 22. Thebiometric information measurement unit 21 measures biometricinformation, such as body temperature and a heart rate. Thecommunication unit 22 includes a communication circuit and externallytransmits the biometric information. The wearable sensors 101 c and 102c are worn on or embedded in a human body. Examples of the wearablesensors 101 c and 102 c include sensor devices such as an activitytracker, a heart rate monitor, a clinical thermometer, a respiratoryrate measuring device, a glucose meter, and a sphygmomanometer.

The wearable sensors 101 c and 102 c also connect to the network 103 viawireless or wired connection. For wireless connection, for example,wireless communication technology, such as Bluetooth (registeredtrademark) and a wireless LAN, is employed. The wearable sensors 101 cand 102 c each pre-store a unique wearable sensor ID into an internalnonvolatile memory or the like. The communication unit 22 externallytransmits the biometric information having the wearable sensor IDattached thereto.

The information input terminals 101 d and 102 d are computer terminals.The information input terminals 101 d and 102 d each include an inputreceiving unit 31 and a communication unit 32. The input receiving unit31 receives input via a keyboard, a pointing device, a touch panel, orthe like. The communication unit 32 includes a communication circuit andexternally transmits information in response to the input. Examples ofthe information to be transmitted include registration information andattribute information. The registration information is information inwhich a home appliance ID of the home appliance, a mobile ID of a mobileowned by a user, and a wearable sensor ID of the wearable sensor for theuser are linked to the user ID. The attribute information is informationin which the age, gender, and the like of the user of the home applianceare associated with the user ID.

The server 104 is configured with a computer which includes acommunication device, storage, a processor, and so on. The server 104 isa device which obtains various pieces of information via the network103, generates information to be provided (provision information), bypredicting behavior of the home appliance user, and provides theinformation presentation device 105 with the provision information. Thevarious pieces of information are transmitted to the server 104 from thehome appliances 101 a and 102 a, the wearable sensors 101 c and 102 c,the information input terminals 101 d and 102 d, and the mobiles 101 band 102 b.

The server 104, in functional terms, includes a communication unit 61, alifestyle information obtaining unit 71, a biometric informationobtaining unit 72, an attribute information obtaining unit 73, aregistration information obtaining unit 74, a location informationobtaining unit 75, a lifestyle information storage unit 81, a biometricinformation storage unit 82, an attribute information storage unit 83, aregistration information storage unit 84, a location information storageunit 85, a behavior prediction unit 91, and a provision informationgenerating unit 92.

Here, the communication unit 61 includes a communication circuit. Thecommunication unit 61 is a function unit which communicates with thedevices connected thereto via the network 103. The lifestyle informationstorage unit 81, the biometric information storage unit 82, theattribute information storage unit 83, the registration informationstorage unit 84, and the location information storage unit 85 are eachconfigured with a storage medium such as a memory and a hard disk.

The lifestyle information obtaining unit 71 has a function of obtaining,via the communication unit 61, the lifestyle information and the homeappliance ID transmitted from the home appliance 101 a or 102 a andaccumulating them in the lifestyle information storage unit 81. Thelifestyle information to be accumulated is attached with timeinformation such as a time at which the lifestyle information ismeasured by the home appliance 101 a or 102 a or a time instant obtainedat the server 104, and is managed as historical lifestyle information.It should be noted that the lifestyle information obtaining unit 71 maybe configured to encompass the reception capability of the communicationunit 61, in which case, the lifestyle information obtaining unit 71obtains the lifestyle information and so on by receiving them.

The biometric information obtaining unit 72 has a function of obtaining,via the communication unit 61, the biometric information and thewearable sensor ID which are transmitted from the wearable sensor 101 cor 102 c, and accumulating them into the biometric information storageunit 82. The biometric information to be accumulated is attached withtime information such as a time at which the biometric information ismeasured by the wearable sensor 101 c or 102 c or a time instantobtained at the server 104, and is managed as historical biometricinformation.

The attribute information obtaining unit 73 has a function of obtaining,via the communication unit 61, the attribute information transmittedfrom the information input terminal 101 d or 102 d, and storing it intothe attribute information storage unit 83. The registration informationobtaining unit 74 has a function of obtaining, via the communicationunit 61, the registration information transmitted from the informationinput terminal 101 d or 102 d, and storing it into the registrationinformation storage unit 84.

The location information obtaining unit 75 has a function of obtaining,via the communication unit 61, the location information transmitted fromthe mobile 101 b or 102 b, and accumulating it into the locationinformation storage unit 85. The location information to be accumulatedis attached with time information such as a time at which the locationinformation is measured by the mobile 101 b or 102 b or a time instantobtained at the server 104, and is managed as historical locationinformation.

By the processor executing a control program which includes a predictionalgorithm for predicting user behavior based on the lifestyleinformation, the biometric information, and the attribute information,the behavior prediction unit 91 implements the following function. Inother words, the function is of predicting behavior of each user by, forexample, estimating patterns of behavior of the user based on thelifestyle information and the biometric information respectivelyassociated with the home appliance ID and the wearable sensor ID whichare linked to the user ID, and the attribute information associated withthe user ID. The behavior prediction unit 91 refers to the registrationinformation stored in the registration information storage unit 84 toobtain correspondence of the lifestyle information, the biometricinformation, the attribute information, and so on with the registrationinformation.

The prediction algorithm is predetermined for use. The algorithm, forexample, analyzes the history of times, at which the home appliance isused by the user, at specific intervals (e.g., per day), based on thelifestyle information, estimates that the user has a habit of using thehome appliance in a specific time slot, and predicts user behavior inthe future, using a result of the estimation.

An example of a useful prediction algorithm is an algorithm whichestimates that a user who has high probability of using a coffee makerin the morning has a habit of drinking coffee at home every morning, andpredicts that the user is likely to drink coffee-related beverage whileaway from home on a day when the user does not drink coffee at home inthe morning of the day.

It should be noted that in the prediction algorithm, as a methodutilizing the history, characteristics may be utilized which are commonto part of a specific time unit, such as a yearly basis, a weekly basis,or a daily basis, that defines a habit of a person. In the case ofweekly basis, for example, characteristics common only to weekdays, suchas from Monday to Friday, and characteristics common only to holidays,such as Saturday and Sunday, may be distinguished from each other toseparately predict time slots where the user conducts a specificbehavior between weekdays and holidays. In the case of yearly basis,characteristics common only to spring and summer and characteristicscommon only to fall and winter are distinguished from each other toseparately predict time slots where the user conducts a specificbehavior between spring and summer and fall and winter.

By the processor executing a control program which includes aninformation generation algorithm for generating provision informationbased on a result of the prediction by the behavior prediction unit 91and the location information, the provision information generating unit92 implements the following function. In other words, the function is ofreferring to the registration information of each user stored in theregistration information storage unit 84 to predict a location where theuser would move to in the future, based on the location informationassociated with the mobile ID linked to the user ID (e.g., based on ahistory which is a set of a plurality of pieces of location informationhaving the measured times attached thereto). The function then predictsuser behavior at a particular location in accordance with the predictionof user behavior in the future which is a result of the prediction bythe behavior prediction unit 91, and generates the provision informationin response to a result of the prediction of the user behavior at theparticular location, and transmits the provision information to theinformation presentation device 105 via the communication unit 61. Itshould be noted that, herein, generating the provision informationincludes selecting, as the provision information, one of previouslyprepared pieces of information.

The information generation algorithm is predetermined. For example, theinformation generation algorithm predicts a location in the future of auser who is predicted to be likely to conduct a specific behavior in thefuture, and generates information to be provided to the user and others,in response to a result of the prediction. It should be noted thatlinear prediction or any other method may be used as an algorithm whichpredicts a location of the user at each time instant in the future,based on historical location information of the user (locationinformation which indicates locations of the user at a plurality of timeinstants).

An example of a useful information generation algorithm is an algorithmwhich predicts a station to which a user predicted to be likely to drinkcoffee-related beverage would move in each time slot in the future, andthen generates information on the number of sales of canned coffee atthe station to be provided to the canned coffee drink distributor, orgenerates an advertisement of canned coffee or information on nearbycafe to be provided to the user.

The information generation algorithm may forecast demand for items andservices which varies in hours, minutes, or seconds, using a pluralityof pieces of location information of a plurality of users. As a result,the information generation algorithm can generate and provideinformation that is necessary to achieve optimal stock of items, provideeffective advertisements, and provide convenient services, for example.It should be noted that the information generated by the informationgeneration algorithm may be different for different users. Each user isallowed to know the information generated by the information generationalgorithm, via a mobile terminal or the like owned by the user.

The information presentation device 105 is a computer terminal. Theinformation presentation device 105 includes a communication unit 52 andan information presentation unit 51. The communication unit 52 includesa communication circuit and receives information transmitted from theserver 104. The information presentation unit 51 presents the receivedinformation by displaying it on a display or outputting it in an audioformat from a loudspeaker. The information presentation device 105 maybe one or more portable smartphones and tablets, a TV installed at home,or a digital signage or the like. It should be noted that a particularoperator may manage the information presentation device 105 and providepresented information as it is or after processing it to anotheroperator, a device owned by a user, and a device installed at aparticular place, for example.

In the following, operations of the devices of the information providingsystem 100 having the above configuration will be described.

FIG. 3 is a flowchart illustrating a home appliance process which isperformed by the home appliance 101 a. It should be noted that the homeappliance process is performed by the home appliance 102 a as well.

As illustrated in the figure, every time user manipulation is conducted(step S301), the home appliance 101 a performs operation in response tothe user manipulation (step S302). In the case where the home appliance101 a is a coffee maker, for example, the home appliance 101 a performsprocessing, such as starts dripping down coffee by heating, in responseto switch operation by the user. It should be noted that depending onthe home appliance 101 a, the home appliance 101 a can operateaccordingly, rather than independently in response to the usermanipulation such as the above-mentioned processing. In the case wherethe home appliance 101 a is a refrigerator, for example, the homeappliance 101 a transitions to a state where a door is open, in responseto user manipulation of opening the door.

In operation, the home appliance 101 a causes the lifestyle informationrecording unit 11 to record the lifestyle information, such as operationof the home appliance 101 a in response to the user manipulation and atime at which the operation is performed, into an internal storagemedium (step S303). In the case where the home appliance 101 a is acoffee maker, the lifestyle information is information indicating, forexample, a time at which a switch is turned on, i.e., a time at whichthe home appliance 101 a starts dripping down coffee.

If transmission conditions are met (step S304), the communication unit12 of the home appliance 101 a transmits the lifestyle information whichis content of the internal storage medium of the home appliance 101.aand the home appliance ID to the server 104 via the network 103 (stepS305). Examples of the transmission conditions include conditions forcollectively transmitting the lifestyle information at predeterminedtime intervals, such as at arrival of a particular time everyday,conditions for collectively transmitting the lifestyle information at atime such as upon receipt of a request from the server 104, andconditions for collectively transmitting the lifestyle information inreal time such as every time a user manipulation is performed. It shouldbe noted that to meet the conditions for transmitting the lifestyleinformation upon receipt of a request from the server 104, the server104 needs to be configured to successively issue a request at given timeintervals, for example.

FIG. 4 is a flowchart illustrating a wearable sensor process which isperformed by the wearable sensor 101 c. It should be noted that thewearable sensor process is performed by the wearable sensor 102 c aswell.

As illustrated in the figure, in the wearable sensor 101 c being worn bythe user, the biometric information measurement unit 21 measures andrecords biometric information, such as body temperature and a heart rateof the user, into an internal storage medium (step S401). Themeasurement and recording of the biometric information are repeatedsuccessively.

If transmission conditions are met (step S402), the communication unit22 of the wearable sensor 101 c transmits the biometric informationwhich is content of the internal storage medium of the wearable sensor101 c and the wearable sensor ID to the server 104 via the network 103(step S403). Examples of the transmission conditions include conditionsfor collectively transmitting the biometric information at predeterminedtime intervals, such as at arrival of a particular time everyday,conditions for collectively transmitting the biometric information at atime such as upon receipt of a request from the server 104, andconditions for transmitting biometric information every time thebiometric information is measured. It should be noted that, in thistransmission conditions also, to meet the conditions for transmittingthe biometric information upon receipt of a request from the server 104,the server 104 needs to be configured to successively issue a request atgiven time intervals, for example.

FIG. 5 is a flowchart illustrating a mobile process which is performedby the mobile 101 b. It should be noted that the mobile process isperformed by the mobile 102 b as well.

As illustrated in the figure, in the mobile owned by the user, thelocation measuring unit 41 having GPS functionalities or the likemeasures the location of the mobile, i.e., a location of the user (stepS501). Then, the communication unit 42 transmits to the server 104location information, which is a result of the measurement, and themobile ID unique to the mobile stored in an internal nonvolatile memoryor the like, via the network 103 (step S502). The measurement andtransmission of location information are repeated successively.

FIG. 6 is a flowchart illustrating an information input terminal processwhich is performed by the information input terminal 101 d. It should benoted that the information input terminal process is performed by theinformation input terminal 102 d as well.

As illustrated in the figure, in the information input terminal 101 d,the input receiving unit 31 receives input which is made via a keyboardor the like by the home appliance user to identify the user ID, the homeappliance ID, the wearable sensor ID, and the mobile ID (step S601). Theinput is provided by direct input or selecting an option. Subsequently,the communication unit 32 transmits the user ID, the home appliance ID,the wearable sensor ID, and the mobile ID which are identified inresponse to the input, as registration information, to the server 104via the network 103 (step S602). The transmission of the registrationinformation is transmitted performed so that the server can identify ahome appliance, a wearable sensor, and a mobile of each user. It shouldbe noted that a user ID only needs to be information whereby a user canbe identified.

In the information input terminal 101 d, the input receiving unit 31receives input of attribute information indicating the age, gender, andthe like of the home appliance user (step S603), and the communicationunit 32 transmits the attribute information to the server 104 via thenetwork 103 (step S604).

FIG. 7 is a flowchart illustrating a server process which is performedby the server 104.

As illustrated in the figure, the registration information obtainingunit 74 of the server 104 receives the registration information from theinformation input terminals 101 d and 102 d via the communication unit61, and stores the received registration information into theregistration information storage unit 84 (step S701). This allows theserver to know correspondence of each user with corresponding homeappliance, wearable sensor, and mobile.

Subsequently, the server 104 repeatedly performs steps S702 to S712 tosuccessively obtain information from each user and successively providethe information presentation device 105 with the provision information.

If the communication unit 61 receives lifestyle information having thehome appliance ID attached thereto (step S702) the lifestyle informationobtaining unit 71 of the server 104 stores the lifestyle information andthe home appliance ID in association into the lifestyle informationstorage unit 81. In other words, the lifestyle information obtainingunit 71 accumulates the lifestyle information and the home appliance IDin association in a database in the lifestyle information storage unit81 (step S703).

If the communication unit 61 receives biometric information having thewearable sensor ID attached thereto (step S704), the biometricinformation obtaining unit 72 stores the biometric information and thewearable sensor ID in association into the biometric information storageunit 82. In other words, the biometric information obtaining unit 72accumulates the biometric information and the wearable sensor ID inassociation in a database in the biometric information storage unit 82(step S705).

If the communication unit 61 receives attribute information (step S706),the attribute information obtaining unit 73 stores the attributeinformation into the attribute information storage unit 83 (step S707).

If the communication unit 61 receives location information having themobile ID attached thereto (step S708), the location informationobtaining unit 75 stores the location information and the mobile ID inassociation into the location information storage unit 85 (step S709).

The behavior prediction unit 91 predicts user behavior in the future byestimating patterns of user behavior, according to the above predictionalgorithm (step S710). The behavior prediction unit 91 refers to theregistration information stored in the registration information storageunit 84 to conduct the estimation and the prediction, based on thelifestyle information stored in the lifestyle information storage unit81, the biometric information stored in the biometric informationstorage unit 82, and the attribute information stored in the attributeinformation storage unit 83.

Subsequently, the provision information generating unit 92 predicts, foreach user, a location to which the user is to move, based on thelocation information stored in the location information storage unit 85.Then, the provision information generating unit 92 refers to theregistration information stored in the registration information storageunit 84 to generate provision information based on a result of theprediction of user behavior corresponding to the predicted location towhich the user is to move, according to the above information generationalgorithm (step S711). Then, the communication unit 61 transmits thegenerated provision information to the information presentation device105 via the network 103 (step S712). The provision informationgenerating unit 92, successively, makes the prediction and generates theinformation, based on the location information or the like successivelyobtained. The information which varies in seconds, in minutes, in hours,in days, or in weeks, for example, is transmitted in real timeconforming to the time unit.

While the information providing system 100 has been described above,with reference to the example given two home appliance users at twohouses, the information providing system 100 can provide more usefulinformation if the information providing system 100 is targeted at alarger number of users, e.g., three or more users. In this case, theserver 104 obtains the lifestyle information, the biometric information,the attribute information, and the location information of each of thelarger number of users, predicts the user behavior and the location towhich the user is to move, and generates and provides information.

It should be noted that the server 104 may predict, for each of aplurality of users (preferably, a greater number of users), thelikelihood that the user performs an action such as obtaining aparticular item or having the benefit of a particular service, andpredict the location to which the user, who is likely to perform such anaction, is to move. This allows forecasting of the degree of demand forthe particular item or the particular service at a particular place in aspecific time slot. Thus, the server 104 may provide effectiveinformation, such as the number of sales of the particular item and anadvertisement of the particular service, based on a result of theprediction.

In order that the information providing system 100 provides theinformation that is related to demand for a particular item or aparticular service at a particular place, the prediction algorithm andthe information generation algorithm may be as follows. In other words,the prediction algorithm estimates, for each of a plurality of users,patterns of behavior of the user from the lifestyle information of theuser, and predicts the likelihood that the user is in need for theparticular item or the particular service at a particular time (may bein a time slot or a multiple time points.) in the future. It should benoted that the prediction algorithm may perform the estimation from thebiometric information and the attribute information, in addition to thelifestyle information. Moreover, the information generation algorithmalso predicts, for each user, the location of the user at the particulartime in the future from historical location information of the user.Then, the information generation algorithm sets the degree of demand forthe particular item or the particular service, based on the number ofusers who would be in the particular place at the particular time in thefuture and whose likelihood of being in need for the particular item orthe particular service is above a certain baseline.

Demand forecasting effectively works if an item or a service that isprovided to the user from the corresponding home appliance 101 a or 102a, which transmit the lifestyle information to the server 104, has thesame attribute as that of the particular item or the particular servicethe demand for which is forecasted. Items having the same attribute orservices having the same attribute include those recognized as been ofthe same type in view of users' preference target.

For example, a coffee maker is a device which provides a user withcoffee having the same attribute as canned coffee. Thus, the informationproviding system 100 can predict, based on coffee maker usage history,that the user, who is estimated to have high inclination to drink coffeeat home every morning, would drink canned coffee elsewhere in thatmorning if the user did not drink coffee at home. Moreover, theinformation providing system 100 can predict a location to which theuser is to move in that morning. Thus, by collecting a large number ofpieces of such user information, the information providing system 100can generate and present information which indicates, for example, thenumber of users who are predicted to come to a particular station and belikely to drink canned coffee on the morning of that day. It should benoted that the information providing system 100 may generate and presentinformation which, for example, is indicated on a map, showing adistribution of current locations or future locations of users who areestimated to have high inclination to drink coffee every morning orthose who are predicted to be likely to drink canned coffee on themorning of that day.

It should be noted that the home appliances 101 a and 102 a may transmitthe lifestyle information to the server 104 via the mobiles 101 b and102 b, respectively.

Moreover, when obtaining the lifestyle information, the biometricinformation, the location information, the attribute information, andthe registration information which are related to the user, the server104 may perform a consent step of obtaining consent from the user foruser privacy. For example, the home appliances 101 a and 102 a, themobiles 101 b and 102 b, the wearable sensors 101 c and 102 c, and theinformation input terminals 101 d and 102 d may perform the consent steponce before transmitting all pieces of information on the user or everytime before transmitting each piece of information. Devices which do notinclude user interfaces may cause another device to alternativelyperform the consent step.

For example, given that the mobile 101 b is a mobile informationterminal which includes a display, the mobile 101 b may obtain theconsent from the user via the graphical user interface (GUI) shown inFIG. 8. This allows the server 104 to obtain, via the GUI, consent fromthe user to obtain the lifestyle information that is obtained from thehome appliance 101.a used by the user, or consent from the user toprovide a service provider 804 (“AAA” in the figure), which isresponsible for the server 104, with the lifestyle information.

Here, on the above-mentioned GUI, the service provider 804 to which thelifestyle information is provided, content 803 of the lifestyleinformation to be provided by the user, service content 802 to beprovided by the service provider 804 may be displayed. This allows theuser to be aware of the purpose for which the user provides theinformation and where the information is provided to, making it easierfor the user to make decision whether to give consent. It should benoted that the above information generation algorithm may be configuredsuch that information as to whether the consent is given from the useris stored in association with the registration information or the likeinto the server and the information providing system 100 provides moreinformation to the user who has given the consent than those who havenot.

Moreover, lifestyle information 903 obtained from the home appliance 101a used by the user may be presented via the GUI shown in FIG. 9, and theconsent step may be performed where the user is allows to select, byclicking on a check box, lifestyle information to be presented to theservice provider 804. FIG. 9 illustrates an example where the homeappliance 101 a is a combination of a coffee maker, a refrigerator, anda toilet, and the mobile 101 b is a mobile information terminal whichincludes a display. The example of FIG. 9 requires consent from the useronly once, thereby alleviating the burden for the user of confirmationoperation.

Moreover, the mobile 101 b, the information input terminal 101 d, theinformation presentation device 105, and the server 104 described abovemay be integrated as a mobile 101 e.

In the following, as a variation of the information providing system100, a configuration will be described where the mobile 101 b, theinformation input terminal 101 d, the information presentation device105, and the server 104 are integrated (the mobile 101.e), focusing ondifferences from the information providing system 100 according to theembodiment 1.

FIG. 10 is a functional block diagram, including the mobile 101 e, andthe home appliance 101 a and the wearable sensor 101 c which cooperatewith the mobile 101.e.

Examples of the mobile 101 e include a mobile terminal such as asmartphone and a tablet, or a vehicle navigation device utilizing GPS ora vehicle having the vehicle navigation device mounted therein.

The mobile 101 e includes an input receiving unit 31 e, a locationmeasuring unit 41 e, a communication unit 61 e, a lifestyle informationobtaining unit 71 e, a biometric information obtaining unit 72 e, anattribute information obtaining unit 73 e, a registration informationobtaining unit 74 e, a location information obtaining unit 75 e, alifestyle information storage unit 81 e, a biometric information storageunit 82 e, an attribute information storage unit 83 e, a registrationinformation storage unit 84 e, a location information storage unit 85 e,a behavior prediction unit 91 e, a provision information generating unit92 e, and an information presentation unit 51 e. The input receivingunit 31 e receives input via a keyboard, a pointing device, a touchpanel, or the like. The location measuring unit 41 e generates locationinformation of the mobile 101 e, utilizing GPS. The communication unit61 e includes a communication circuit and communicates with the homeappliance 101 a and the wearable sensor 101 c which are connected to thecommunication unit 61 e via the network 103.

Here, the lifestyle information storage unit 81 e, the biometricinformation storage unit 82 e, the attribute information storage unit 83e, the registration information storage unit 84 e, and the locationinformation storage unit 85 e are each configured with a storage mediumsuch as a memory and a hard disk.

The lifestyle information obtaining unit 71 e has a function ofobtaining, via the communication unit 61 e, the lifestyle informationand the home appliance ID transmitted from the home appliance 101 a, andaccumulating them into the lifestyle information storage unit 81 e. Thelifestyle information to be accumulated is attached with timeinformation such as a time at which the lifestyle information ismeasured by the home appliance 101 a or a time instant obtained at themobile 101 e, and is managed as historical lifestyle information. Itshould be noted that the lifestyle information obtaining unit 71 e maybe configured to encompass the reception capability of the communicationunit 61 e, in which case, the lifestyle information obtaining unit 71 eobtains the lifestyle information and so on by receiving them.

The biometric information obtaining unit 72 e has a function ofobtaining, via the communication unit 61 e, the biometric informationand the wearable sensor ID transmitted from the wearable sensor 101 c,and accumulating them into the biometric information storage unit 82 e.The biometric information to be accumulated is attached with timeinformation such as a time at which the biometric information ismeasured by the wearable sensor 101 c or a time instant obtained at themobile 101 e, and is managed as historical biometric information.

The attribute information obtaining unit 73 e has a function ofidentifying, in response to the input received by the input receivingunit 31 e, attribute information where the age, gender or the like ofthe user of the home appliance 101 a are associated with the user ID,and storing the attribute information into the attribute informationstorage unit 83 e.

The registration information obtaining unit 74 e has a function ofidentifying, in response to the input received by the input receivingunit 31 e, registration information where the home appliance ID of thehome appliance 101 a and the wearable sensor ID of the wearable sensor101 c are linked to the user ID, and storing the registrationinformation into the registration information storage unit 84 e.

The location information obtaining unit 75 e has a function of obtaininglocation information from the location measuring unit 41 e andaccumulating it into the location information storage unit 85 e. Thelocation information to be accumulated is attached with time informationsuch as a time at which the location information is measured, and ismanaged as historical location information.

By the processor executing a control program which includes a predictionalgorithm for predicting user behavior based on the lifestyleinformation, the biometric information, and the attribute information,the behavior prediction unit 91 e implements the following function. Inother words, the function is of predicting user behavior by referring tothe registration information to estimate patterns of user behavior basedon the lifestyle information and the biometric information respectivelyassociated with the home appliance ID and the wearable sensor ID whichare linked to the user ID, and the attribute information that isassociated with the user ID. The prediction algorithm is the same asthat performed by the behavior prediction unit 91 of the server 104described above, for example.

By the processor executing a control program which includes aninformation generation algorithm for generating provision informationbased on a result of the prediction by the behavior prediction unit 91 eand the location information, the provision information generating unit92 e implements the following function. In other words, the function isof referring to the registration information of each user to predict alocation where the user would move to in the future, based on thelocation information associated with the mobile ID linked to the user ID(e.g., a history which is a set of the location information having themeasured times attached thereto). The function then predicts userbehavior at a particular location in accordance with the prediction ofuser behavior in the future which is a result of the prediction by thebehavior prediction unit 91 e, and generates the provision informationin response to a result of the prediction of the user behavior at theparticular location, and presents the provision information via theinformation presentation unit 51 e. It should be noted that theinformation generation algorithm is the same as that performed by theprovision information generating unit 92 of the server 104 describedabove, for example.

The information presentation unit 51 e includes a display or aloudspeaker, and has a function of displaying the provision informationprovided by the provision information generating unit 92 e to thedisplay or outputting it from the loudspeaker.

FIG. 11 is a flowchart illustrating the mobile process which isperformed by the mobile 101 e.

As illustrated in the figure, the registration information obtainingunit 74 e of the mobile 101 e receives, via the input receiving unit 31e, input from the home appliance user made via a keyboard or the like toidentify the user ID, the home appliance ID, and the wearable sensor ID(step S1101).

Then, the registration information obtaining unit 74 e stores the userID, the home appliance ID, and the wearable sensor ID, which areidentified in response to the input, as registration information, intothe registration information storage unit 84 e (step S1102).

The attribute information obtaining unit 73 e receives input ofattribute information indicating, for example, the age and gender of theuser via the input receiving unit 31 e (step S1103), and stores theattribute information into the attribute information storage unit 83 e(step S1104).

Subsequently, the mobile 101 e repeatedly executes steps S1105 to S1113,thereby successively obtaining information from the user andsuccessively present the provision information.

If the communication unit 61 e receives the lifestyle information havingthe home appliance ID attached thereto (step S1105), the lifestyleinformation obtaining unit 71 e of the mobile 101 e accumulates thelifestyle information and the home appliance ID in association in thelifestyle information storage unit 81 e (step S1106).

If the communication unit 61 e receives the biometric information havingthe wearable sensor ID attached thereto (step S1107), the biometricinformation obtaining unit 72 e accumulates the biometric informationand the wearable sensor ID in association in the biometric informationstorage unit 82 e (step S1108).

The location measuring unit 41 e of the mobile 101 e measures thelocation of the mobile 101 e, i.e., the location of the user who ownsthe mobile 101 e (step S1109), and the location information obtainingunit 75 e obtains and accumulates location information, which is aresult of the measurement, in the location information storage unit 85 e(step S1110).

The behavior prediction unit 91 e predicts user behavior in the futureby estimating patterns of user behavior, according to the aboveprediction algorithm (step S1111). The behavior prediction unit 91 erefers to the registration information stored in the registrationinformation storage unit 84 e to conduct the estimation and theprediction based on the lifestyle information stored in the lifestyleinformation storage unit 81 e, the biometric information stored in thebiometric information storage unit 82 e, and the attribute informationstored in the attribute information storage unit 83 e.

Subsequently, the provision information generating unit 92 e predicts alocation to which the user is to move, based on the location informationstored in the location information storage unit 85 e. Then, theprovision information generating unit 92 e refers to the registrationinformation stored in the registration information storage unit 84 e togenerate provision information based on a result of the prediction ofthe user behavior corresponding to the predicted location to which theuser is to move, according to the above information generation algorithm(step S1112). The information presentation unit 51 e then presents thegenerated provision information (step S1113).

It should be noted that the home appliance ID, the wearable sensor ID,the mobile ID, and the user ID may not be used if, rather than aplurality of users using the mobile 101 e in turn, the mobile 101 e isused only by one user.

In the information providing system, the mobile 101 e can provide a userowning the mobile 101 e with information such as a pinpointadvertisement highly effective to the user, according to the location ofthe mobile 101 e, i.e., the location of the user. The highly effectivepinpoint advertisement refers to targeted advertisements to users whoseem more likely to purchase products than others. This can providehopes for more sales, and, additionally, provide effects on reducingpower consumption of the devices which provide information.

In the foregoing example, the mobile 101 e (see FIG. 10) generates theprovision information as a result of the prediction which is made by thebehavior prediction unit 91 e and the provision information generatingunit 92 e based on the lifestyle information and the locationinformation, for example. Then, the information presentation unit 51 epresents the provision information. Moreover, in the example describedfurther above, similarly, the provision information is generated as aresult of the prediction which is made by the behavior prediction unit91 and the provision information generating unit 92 of the server 104based on the lifestyle information and the location information, forexample. Then, the information presentation device 105 presents theprovision information. A system configuration is also possible whichprovides, instead of presenting such information, control of theoperation of each device by supplying control information forcontrolling the device in response to a result of the prediction madebased on the lifestyle information and the location information, forexample.

Moreover, the wearable sensors 101 c and 102 c may be removed from theabove-described information providing system and the biometricinformation may not be utilized. However, using the biometricinformation allows more accurate estimation of the user's situation and,as a result, the information providing system can provide more usefulinformation. Moreover, similarly, the information input terminals 101 dand 102 d may be removed from the above-described information providingsystem and the attribute information and the registration informationmay not be utilized. When information is generated and provided based onlifestyle information of one user, there is no need to identify theuser. Thus, the registration information can be unnecessary. Also, theidentification information such as the home appliance ID, the wearablesensor ID, and the mobile ID can be replaced with the user ID, and ifthe information providing system is targeted at one user only, theidentification information such as the home appliance ID is unnecessary.

It should be noted that the home appliances 101 a and 102 a, thewearable sensors 101 c and 102 c, the mobiles 101 b and 102 b or themobile 101 e in the above-described information providing system (seeFIGS. 2 and 10) each may include means for identifying the user byreceiving input of the user ID or by personal identification. Examplesof the personal identification include face recognition, fingerprintrecognition, and iris recognition. This enables to distinguish betweenusers, allowing more accurate estimation of each user's situation. Thus,more useful information is provided. Moreover, the result of thepersonal identification may be used as replacement of the registrationinformation described above.

Moreover, the functional partitioning between the behavior predictionunits 91 and 91 e and the provision information generating units 92 and92 e in the above-described information providing system is merelyillustrative. The allocation of functions is changeable and the behaviorprediction units 91 and 91 e and the provision information generatingunits 92 and 92 e may be integrated.

In the following, the embodiment 2 and the subsequent embodiments willdescribed, with reference to specific examples of the informationproviding system described in the embodiment 1. In the embodiment 2 andthe subsequent embodiments, the description already set forth in theembodiment 1 may not be repeated.

Embodiment 2

Hereinafter, coffee makers by way of example of the home appliances 101a and 102 a in the above-described information providing system 100 willbe mainly described, with reference to FIGS. 1 and 2.

In the information providing system according to the present embodiment,home appliances 101 a and 102 a, which are coffee makers, each includemeans (a detection mechanism) for detecting use of the home appliance bya user. Examples of the detection mechanism include means for monitoringpower consumption, and a temperature sensor installed in the homeappliance where heated when in use. A result of the detection (e.g., adetection time) by the detection mechanism is recorded as the lifestyleinformation into the lifestyle information recording unit 11, and thecommunication unit 12 transmits to the server 104 the lifestyleinformation and the home appliance ID for identifying the coffee maker,via the network 103. This allows the server 104 to obtain coffee makerusage history of each user (individual) or each house. The server 104can predict user behavior from the coffee maker usage history, andprovide the information presentation device 105 with information that isgenerated based on the prediction. The information presentation device105 can be installed at, for example, public transportation or itssurroundings and used.

It should be noted that the coffee maker may include a mechanism forrecording information on power that is consumed by the coffee maker inuse or a generated quantity of coffee. This allows information on aquantity of coffee drunk by the user to be included in the lifestyleinformation and utilized for the user behavior prediction by the server104.

For example, a prediction algorithm assuming that the user would want todrink coffee after a given time has elapsed since the user has last usedthe coffee maker allows the server 104 to predict how much likely theuser would drink coffee in the near future. From a result of theprediction and a result of predicting a location to which the user is tomove in the near future, the server 104 can compute likelihood that theuser purchases canned coffee at a store in a given station when the usercomes to the station, generate provision information indicating thelikelihood that the user purchases canned coffee, and the informationpresentation device 105 presents the provision information.

It should be noted that it can be estimated, for each user, whether theuser is currently using public transportation, based on user locationhistory and route maps of public transportation such as railways (pathlocation information). Furthermore, use of location information ofstations allows estimation of a station the user is to arrive at whattime. Use of timetable information can further improve the precision inthat estimation. It should be noted that the information generationalgorithm may estimate, based on the historical location information ofthe user, that there is high probability that the user uses publictransportation of the same path at the same time of the same day of aweek, and predict a station at which the user is to subsequently make atransfer.

According to this, by obtaining lifestyle information, for each user, asto whether the user, who uses a station of public transportation, hasused a coffee maker at home before going out, the likelihood that theuser purchases canned coffee at a shop in the station can be predicted.Thus, for example, hourly sales of canned coffee at the station can bepredicted, and presented at the information presentation device 105.

Also, more effective advertisements can be implemented, such asswitching an advertisement presented by the information presentationdevice 105 at a given station to an advertisement of canned coffee for atime slot during which a large number of users, among those who use thegiven station, are predicted to be likely to purchase canned coffee.

Moreover, from coffee maker history of the users, the predictionalgorithm may distinguish between users who use coffee makers morefrequently than a certain baseline and those who use coffee makers lessfrequently than the certain baseline. The prediction algorithm may thendetermine that a user who uses the coffee maker more frequently than thecertain baseline and did not use it before going out (i.e., a user in asituation where it has been a given time or more since the user lastused the coffee maker) is likely to purchase canned coffee while awayfrom home. Then, the information generation algorithm may generateinformation reflected the situation of the user to the likelihood thatthe user purchases canned coffee at a station.

Moreover, the wearable sensors 101 c and 102 c may be devices whichmeasure a heart rate of a user (individual) and the prediction algorithmmay predict that the user having a lower heart rate is more likely topurchase canned coffee. This is because it can be estimated that anindividual having a low heart rate measured is sleepy. Moreover, thewearable sensors 101 c and 102 c may be devices which have capability ofmeasuring body temperature of a user (individual), and the predictionalgorithm may predict that a user having a higher body temperaturemeasured is more likely to purchase canned coffee. This is because auser having a high body temperature measured can be estimated to besleepy. A function may be previously set based on statistics and thelike on the relationship between the biometric information, such as thebody temperature and a heart rate, and the likelihood that the userpurchases canned coffee, and the prediction algorithm may predict thelikelihood that the user purchases canned coffee, based on the biometricinformation and the function.

Moreover, in addition to the coffee maker, the home appliances 101 a and102 a according to the present embodiment may each include a sleepmonitor (means which can measure sleep time) having communicationfunctionalities. In this case, the lifestyle information includes sleeptime, in addition to the coffee maker usage. The prediction algorithmmay reflect sleep time to estimate that a user who has fewer hours sleptthe previous night is more likely to purchase canned coffee on the nextday. This allows highly precise prediction on the likelihood that theuser purchases canned coffee from the information of the coffee makerusage before the user goes out and the information on hours slept theprevious night.

Moreover, an electronic payment system or the like may be used.Specifically, history of canned coffee purchase for each user(individual) at each station may be stored in the electronic paymentsystem or the like, collected at the server and used in the predictionalgorithm. Moreover, the prediction algorithm may distinguish betweenusers who often purchase canned coffee and those who rarely purchasecanned coffee to predict that a user who often purchases canned coffeeand did not use a coffee maker before going out is likely to purchasecanned coffee at a station. As a result, the information generationalgorithm can generate information on sales of canned coffee at thestation and the information presentation device 105 can presentinformation based on a result of sales forecasting. Moreover, productnames of canned coffee previously purchased by each user may be storedin the electronic payment system or the like and the informationgeneration algorithm may generate a result of sales forecasting of eachproduct, using the stored product names. As a result, the informationpresentation device 105 can present advertisements of canned coffee(product names) that may be sold on that day, by digital signage or thelike installed at the station.

Moreover, in addition to the coffee maker, the home appliances 101 a and102 a may each include a lighting fixture having communicationfunctionalities. This allows the server 104 to collect lifestyleinformation also on lighting-on history. The prediction algorithm mayestimate that the user has an infant in the household if the lighting ison every three to four hours at night, for example, and predict that thelikelihood that the user purchases canned coffee is markedly low if theuser is a woman. This allows more accurate sales forecasting of cannedcoffee. Moreover, a household with an infant can be derived fromlighting on-time, and if the family is of a plurality of residents(users), for example, whether the resident (user) was hospitalized forgiving birth or visited a hospital can be determined for each residentfrom GPS history of the resident. Thus, whether a given user is a motheror a person who is other than a mother and living in the house can bedetermined. Moreover, the attribute information input to the informationinput terminal 101 d may be information, such as, preference or whethera user is a mother, in addition to age and gender. This can determineand use the prediction algorithm to more accurately predict thelikelihood of purchasing canned coffee based on the attributeinformation which is preference or whether the user is a mother of aninfant.

The example of the information providing system has been described whichprovides information on sales forecasting of canned coffee where thehome appliance 101 a and 102 a each include the coffee maker. The homeappliance as a coffee maker is intended to supply coffee in connectionwith demand for coffee. Hence, the sales forecasting of canned coffeehas a given connection with the sales of a product which is cannedcoffee, that is, demand for coffee, and is thus effective.

As such, the information providing system can predict user behavior(e.g., purchasing an item, having the benefit of a service) inconnection with a particular item or a particular service which has thegiven connection with an intended use of the home appliance 101 a or 102a, and forecast demand for the particular item or particular service.The information providing system can then provide information that isrelated to the particular item or particular service. In this case, thehome appliance has functionalities of providing an item having the sameattribute as the particular item, or functionalities of providing aservice having the same attribute as the particular service.

In the following, other examples of the information providing systemwill be described.

For example, the home appliances 101 a and 102 a may be toasters thathave communication functionalities for externally communicating toasterusage. The toaster usage may be measured by monitoring power consumed bythe toaster. The server 104 obtains information as to whether a userused the toaster before going out, via the network 103. The predictionalgorithm predicts that a user who missed breakfast is likely to want asnack or the like in the near future. Then, the information generationalgorithm estimates the number of users who missed breakfast and are tocome to a particular station for each time slot and predicts sales ofsandwiches (snack) and generates provision information reflected thesales of sandwiches. This, for example, generates provision informationin which the percentage of time for which an advertisement of sandwichesis displayed is varied versus an advertisement of another product inresponse to a result of the prediction. This can display advertisementswhich are effective to users of the station on a given day or in a giventime slot, using the information presentation device 105, such asdigital signage, installed at the station, for example, changing thepercentages of time for which the advertisement of sandwiches isdisplayed versus the advertisement of another product on a daily basisor for each time slot.

Moreover, the home appliances 101 a and 102 a may be other kitchenappliances than coffee makers and toasters. For example, by collectingthe use history of kitchen appliances, such as induction cooktops andmicrowave ovens, the server 104 can estimate, by the predictionalgorithm, a meal time slot of each user and predict a time at which theuser would have a meal in the future. In this case, the informationgeneration algorithm may also forecast demand for food-related productsat a particular place, food services at restaurants, and the like, basedon the estimation of a location to which the user moves at a time whenthe user wants food. Then, the information generation algorithm maygenerate provision information (such as advertisements of products andservices) to be presented by the information presentation device 105installed at the particular place, based on a result of the demandforecasting. The provision information may include informationrepresenting the demand in volume.

It should be noted that in the case where the wearable sensors 101 c and102 c are glucose meters, the prediction algorithm can more accuratelyestimate the meal time, based on changes in blood glucose level of theuser, in addition to the lifestyle information obtained from kitchenappliance, for example. Moreover, the wearable sensors 101 c and 102 cmay be devices which measure a sound uttered by a user wearing thewearable sensor and transmits the sound as the biometric information.This allows, in the prediction algorithm, determination of, for example,the presence and absence of conversation, thereby determining whetherthe user is moving alone or the user is moving in a group. Thus, theprediction algorithm can more accurately predict whether the user is ina state where the user is likely to purchase a product or the like.Moreover, the wearable sensors 101 c and 102 c may be devices whichmeasure and transmit, as the biometric information, the activity and arate of steps taken, per unit time, by the user wearing the wearablesensor, for example. This allows, in the prediction algorithm,estimation of a level of user fatigue that has some correlation with therate of steps and the activity. Thus, the prediction algorithm can moreaccurately predict whether the user is in a state where the user islikely to purchase nutritional drinks, sports drinks, or the like.

Moreover, the home appliances 101 a and 102 a may be TVs or radios whichhave communication functionalities, rather than being the devices forcooking and providing foods. In this case, the server 104 can obtainlifestyle information, for each user, as to whether the user used the TVor the radio before going out, via the network 103. Accordingly, theprediction algorithm may predict a user, who missed a news program onthe TV or the radio before leaving for work, is likely to purchase anewspaper at a station, for example. Moreover, the informationgeneration algorithm may generate, for each station, informationindicating a time slot where a large number of users who are likely topurchase newspapers are present. This allows a vendor or the like tograsp, by seeing the information presentation device 105, newspapersales at each station, thereby efficiently distributing newspapers. Inother words, the information generation algorithm may be determined toforecast hourly sales of products and calculate the number of(predicted) stocks at each station, and cause the informationpresentation device 105 to display information generated based on theprediction and calculation. This allows proper inventory control such asproviding inventory to a station having inventory shortage or to astation at inventory shortage from another station.

Moreover, the home appliances 101 a and 102 a may each be a bath modulehaving communication functionalities, which can detect whether the bathis used, using an occupancy sensor which detects a bath or means formeasuring use history of a lighting fixture. In this case, the server104 can obtain lifestyle information, for each user, which indicates abath situation of the user via the network 103. Thus, the predictionalgorithm may, for example, estimate that a user, who did not have abath on the previous day, is likely to have a slight cold, and predictthat the user is likely to purchase cold medicines or nutritionaldrinks. This allows more effective advertisements, such as providing, ifthe user has a slight cold, an increased display time of advertisementsof cold medicines and nutritional drinks by digital signage installed atstations. It should be noted that storing a long (e.g., a period longerthan a month) record of a bath history of each user in the server 104allows more accurate estimation of the likelihood that a user, who didnot have a bath on the previous day, has a slight cold, in accordancewith whether the user has a habit of having a bath everyday. In otherwords, the display time of advertisements of cold medicines ornutritional drinks can be more effectively set. In the case where thewearable sensors 101 c and 102 c have body temperature measurementfunctionalities, the server 104 can use information on body temperaturecollected to more accurately estimate whether a user has a slight cold.Then, given that the information presentation device 105 is a terminalwhich is carried with a user, the information presentation device 105may effectively provide the user, who is estimated to have a slightcold, with advertisements of cold medicines and nutritional drinks.

Moreover, in the specific examples described in the present embodiment(examples where the home appliances are coffee makers and others) also,the mobile 101 b, the information input terminal 101 d, the informationpresentation device 105, and the server 104 shown in FIG. 2 may beintegrated as the mobile 101 e. In other words, the mobile 101 e, suchas a smartphone or a tablet, may have the following functions, inaddition to the function of detecting the location of the mobile 101 e,i.e., the location of the user owning the mobile 101 e. The functionsinclude a function of inputting the attribute information of the user, afunction of predicting user behavior based on the prediction algorithm,a function of generating information to be provided to the user based onthe information generation algorithm, and a function of presenting thegenerated information to the user (see FIG. 10). This allows the mobile101 e, which detects the location of the user, to effectively presentthe information (pinpoint advertisement) to the user.

Moreover, the information (pinpoint advertisement) to be presented tothe user by the devices (the information presentation device 105, themobile 101 e) which have information presentation functions may bepresented only to, among the users who provide the lifestyle informationand the location information, users who are wishing for distribution ofpinpoint advertisements, for example. For example, if such a userinstalls application which distributes pinpoint advertisements tosmartphones and tables, a pinpoint advertisement is displayed on thedevice when the application is launched, for example.

Pinpoint advertisements allow the user to selectively receiveadvertisements of products which the user is likely to purchase, therebyreducing the number of times the user checks advertisements. Thepinpoint advertisements can also reduce power consumed by the devices(such as a smartphone) that have the information presentation functions.

FIG. 12 shows an example display displaying a pinpoint advertisement ona smartphone 304, by way of example display of the devices which havelocation detection function and the information presentation function.

The example display shown in FIG. 12 displays advertisements ofnutritional drinks to a user who is determined to have a slight cold.The pinpoint advertisement, as illustrated in the figure, displays aproduct name 302 of a product advertized and an image 303, such as apicture of the product advertized, and provides description of condition301 of the user, which is determined based on the lifestyle informationand the biometric information. This allows the user, who receives theadvertisement, to clearly understand the reason why the advertisedproduct is recommended, thereby further motivating the user to purchasethe product. Moreover, privileged information 306, such as discount anda privileged service, intended for the user who receives theadvertisement may be provided. This can achieve effects of furthermotivating the user to purchase the product. Moreover, the privilegedinformation 306 allows reaching a greater number of users who arewishing for distribution of pinpoint advertisements.

Moreover, information 305 on the location of a shop may be displayedbased on user's location information obtained by a smartphone having alocation detection function. This can save the user, who receives theadvertisement, from searching a shop, thereby further increasing thelikelihood that the user purchases the product. Moreover, although notshown, map information (image) of the shop may be additionallydisplayed. A nearest shop may be displayed, considering the currentlocation of the user and a path of travel of the user in the future.Moreover, shops which have margins for inventory may be preferentiallydisplayed, based on shops' inventory information.

Embodiment 3

Hereinafter, toilets by way of example of the home appliances 101 a and102 a in the above-described information providing system 100 will bemainly described, with reference to FIGS. 1 and 2.

In the information providing system according to the present embodiment,home appliances 101 a and 102 a which are toilets (toilet bowls) inhouses, each have communication functionalities and include means (adetection mechanism) for detecting use of the toilet by each user(individual). Examples of the detection mechanism include an occupancysensor using an infrared, and a flow sensor which detects waste water.The toilet may include biometric identification means for separatelyidentifying users in the house. This records the toilet usage history(e.g., time of use) for each user as the lifestyle information into thelifestyle information recording unit 11, and the communication unit 12transmits the lifestyle information and a home appliance ID foridentifying the toilet to the server 104 via the network 103. The server104 can obtain toilet usage history for each user (individual) or foreach house. The server 104 can predict user behavior from the toiletusage history, and provide the information presentation device 105 withinformation which is generated based on the prediction.

A prediction algorithm for predicting user behavior may distinguishapplications of use of toilet (stool and urine) and refer to the toiletusage history, based on a time of stay for which the user uses thetoilet. Distinguishing applications of use of toilet as such allows moreaccurate estimation of a situation of the user. If the toilet,additionally, includes means for conducting component analysis of bodywaste, the lifestyle information can be recorded more accuratelydistinguishing between stool and urine, thereby allowing the server 104to more accurately estimate the situation of the user.

It should be noted that the home appliances 101 a and 102 a may not behousehold toilet bowls themselves but may each be a lighting fixture ora ventilating fan that has communication functionalities in the lavatoryin the house. Using the use history of a lighting fixture or aventilating fan as the lifestyle information allows the server 104 toestimate toilet usage of each user from the lifestyle information.Moreover, the home appliances 101 a and 102 a may not be toilet bowlsand may be devices which include a toilet seat having communicationfunctionalities. The lifestyle information which indicates use of toiletat home and obtained by the device detecting a sitting status of theuser is sent to the server 104.

According to the information providing system in which the server 104collects the use history of toilet at home for each user as thelifestyle information, the prediction algorithm executed by the server104 can predict whether the user is in a state where the user wants touse a lavatory (a stall) while away from home. Moreover, the informationgeneration algorithm executed by the server 104 can estimate for eachuser as to where the user who wants to use a lavatory (stalls) istraveling to, based on the historical location information of the user.The information generation algorithm can then predict a busy status of alavatory (e.g., a waiting time for a lavatory (stall)) on a publictransportation-by-public transportation basis, such as rails, or on astation-by-station basis. Information indicating the predicted busystatus of a lavatory on the public transportation-by-publictransportation basis or on the station-by-station basis is transmittedfrom the server 104 to the information presentation device 105 anddisplayed on a screen or output in audio by the information presentationdevice 105. According to the current location of the user, the provisioninformation generating unit 92 may narrow down the information which isgenerated by the information generation algorithm and indicates a busystatus of a lavatory at a station to information on a station nearestthe user, and distribute the information to the information presentationdevice 105 which is a smartphone or the like of the user.

In the following, an example will be described where the informationpresentation device 105 is a mobile terminal owned by the user, such asmartphone, focusing on a busy status of a lavatory at a station.

FIG. 13 shows an example display displaying busy statuses of lavatoriesat stations on a smartphone 501.

On the screen of the smartphone 501, as illustrated in the figure, boxes502 separated station by station are displayed, and a station name 510is displayed in each box 502. The stations displayed herein aredetermined based on, for example, location information of a user. If theuser is on a train, the next stop (ABC station in FIG. 5) and the nextnext stop (XYZ station in FIG. 5) may be displayed. This allows the userto check for a lavatory at a station nearest the current location,thereby allowing the user to check for a lavatory which the user canarrive in a shorter time. If the user is other than being on a train (ata station), the station the user is currently at and the surroundingstations may be displayed.

Moreover, in the box 502 of a station, boxes 503 of lavatories may bedisplayed, showing all lavatories (stalls) installed at that station.This allows the user to check for lavatory usage in a shorter time.Moreover, location information (or lavatory name) 506 may be displayed,showing the location where the lavatory is installed. This allows theuser to use a lavatory in a shorter time. Moreover, a FIG. 505 may bedisplayed, showing usage of lavatory stalls. FIG. 13 shows the number ofstalls in use divided by the number of stalls installed at the lavatory.This allows the user to be aware of the current busy status of eachlavatory. Moreover, the backgrounds of the boxes 503 of lavatories maybe different in color or pattern, in accordance with the usage of stallsof lavatories. This can clearly covey the busy status of each lavatoryto the user.

Moreover, a subjective rating value 508 obtained from users who usedlavatories may be displayed. For example, users can subjectively ratelavatories after using them and an average value of neatness andcleanness of the lavatories can be displayed. This allows the user toselect a subjectively highly rated (neat or clean) lavatory.

It should be noted that the wearable sensors 101 c and 102 c may recordbiometric information such as a heart rate of a user using a lavatory,and transmit the biometric information to the server 104. Accordingly,the server 104 may estimate the surges of sympathetic nerves andparasympathetic nerves of the user, based on the biometric informationsuch as the heart rate, and use a result of the estimation as metrics asto whether a lavatory is relaxing, for example, to generate a result ofthe rating of each lavatory as provision information. This allows theuser to select an objectively highly rated (relaxing) lavatory. Whilethe subjective rating has an advantage of allowing a large number ofmeasures to be set, the objective rating using the biometric informationobtained from the wearable sensors worn by the user has an advantage ofhaving more reliability than the subjective rating.

Moreover, the prediction algorithm or the information generationalgorithm used by the server 104 may predict a station where each useris to get off a train, based on the historical location information ofthe user. Estimating, for each user, that the user often uses a publictransportation of the same path at the same time on the same day of aweek allows more accurate prediction of the busy status of a lavatory.Moreover, information (busy-level fluctuation information) in which abusy status of each lavatory in the future is predicted based oninformation on user's stops may be displayed on the smartphone 501 giventhat the information presentation device 105 is the smartphone 501.Busy-level fluctuation information 504 may be displayed by an arrow, forexample, as shown in FIG. 13. The larger arrow represents an increase inbusy status in the future, allowing the user to be aware of moreavailable lavatories. In other words, at a station, the user can use alavatory the waiting time for which is a shorter, avoiding busylavatories.

Moreover, a FIG. 507 may be displayed, which indicates the number of theother users who are on the same train, checking the busy status oflavatories by mobile terminals or the like. This allows the user to bemore accurately aware of an available lavatory.

Moreover, the prediction algorithm may compute the probability of use oflavatories at stations for each user, using the lifestyle information(history) or the biometric information of the user, for example.Accordingly, the information generation algorithm may compute, for eachuser, the likelihood, for each station, that the user uses a lavatoryand a time of usage of the lavatory, based on the probability of use oflavatories at stations and the location information (history), andgenerate the busy-level fluctuation information 504 of lavatories ateach station that reflects a result of the computation. Moreover, theinformation generation algorithm may generate the busy-level fluctuationinformation 504 so that a busy level of a lavatory at a station wherethe probability the user gets off a train is high is increased (thearrow is extended) based on daily historical location information of theuser, for example. This allows the user to be more accurately aware ofan available lavatory.

Moreover, while in FIG. 13, the example is given where the busy-levelfluctuation information 504 for each station is displayed on thesmartphone 501, busy-level fluctuation information for each lavatory(stalls) may be displayed. This allows the user to be more accuratelyaware of an available lavatory. For example, based on information ondaily lavatory usage for each user, a lavatory that the user has used inthe past may be determined to be one that the user is likely to use, andthe busy-level fluctuation information which includes an increased busylevel of the lavatory (the arrow is extended) may be displayed. Thisallows the user to be more accurately aware of an available lavatory.

Moreover, the information generation algorithm may predict that the useris likely to use a lavatory at a location closer to the car where theuser is on, based on information on the car where the user is on, andgenerate busy-level fluctuation information. This allows the user to bemore accurately aware of an available lavatory.

Moreover, using means for storing a long-term (e.g., a period of a weekor more) use history of a lavatory (at home or a station, preferablyboth) for each user, the prediction algorithm may predict the likelihoodthat the user uses a lavatory, based on the use history. For example,the prediction algorithm may distinguish between users who often use alavatory before and after leaving for work and users who rarely use alavatory before and after leaving for work to estimate personal habitsand predict that a user, who is estimated to often use a lavatory athome before leaving for work and has missed to use the lavatory at home,is more likely to use a lavatory at a station.

Moreover, the wearable sensors 101 c and 102 c may be heart ratemonitors each of which transmits to the server 104 biometric informationindicating a heart rate of a user wearing the wearable sensor. In thiscase, the prediction algorithm executed by the server 104 may estimatethat a user who has a higher heart rate is more likely to be in a statewhere the user wants to go to a lavatory to predict that the user islikely to go to a lavatory in the near future.

Moreover, the wearable sensors 101 c and 102 c may be devices whichmeasure perspiration of a user wearing the wearable sensor to transmitto the server 104 biometric information indicating the perspiration. Inthis case, the prediction algorithm may estimate that a user whoperspires more than usual is in the state where the user wants to go toa lavatory to predict that the user is likely to go to a lavatory in thenear future.

Moreover, the wearable sensors 101 c and 102 c may be devices whichmeasure blood pressure of a user wearing the wearable sensor to transmitto the server 104 biometric information indicating the blood pressure.In this case, the prediction algorithm may estimate that a user who hasa higher blood pressure than usual is in the state where the user wantsto go to a lavatory to predict that the user is likely to go to alavatory in the near future.

Moreover, the information generation algorithm executed by the server104 may estimate a train or a vehicle where each user, who is predictedto be likely to go to a lavatory, is riding, based on the locationinformation of the user to generate busy-level fluctuation informationso that the busy level of a lavatory increases at a station near thelocation of the user at a time the user exits. The server 104 transmitsthe busy-level fluctuation information to a smartphone or the like ofthe user, allowing the user to be more accurately aware of an availablelavatory.

As described above, the server 104 executes the prediction algorithm topredict the user's probability of using a lavatory at a station. Then,the server 104 executes the information generation algorithm to generatethe busy-level fluctuation information 504 integrating the user'sprobability of using a lavatory at a station and the information that isestimated from daily historical location information of the user, suchas the user's stop or the location of a lavatory often used by the user.Due to this, the busy-level fluctuation information 504 indicates aresult of more accurate prediction of the information on the busy levelof a lavatory. Moreover, the busy-level fluctuation information 504 canbe transmitted to and presented on the smartphone 501 of the user. Thisallows the user using the smartphone 501 to be more accurately aware ofan available lavatory.

Moreover, the information providing system according to the presentembodiment may further include an environmental information acquisitionterminal which is connected to the network 103. The environmentalinformation acquisition terminal detects air temperature andcommunicates it to the server 104. The provision information generatingunit 92 of the server 104 may estimate that lower air temperatureincreases the busy level of a lavatory, based on the information on airtemperature, and generate the busy-level fluctuation information 504that reflects the air temperature. The server 104 transmits thebusy-level fluctuation information 504 to the smartphone 501, therebyallowing the user to be more accurately aware of the fluctuation in busylevel on a lavatory.

Moreover, in the information providing system according to the presentembodiment, the home appliances 101 a and 102 a each may include, inaddition to the toilet, a coffee maker which includes communicationfunctionalities. This allows coffee maker usage to be collected as thelifestyle information at the server 104, in addition to the toiletusage. Since beverages such as coffee have high diuretic effects, if theuser is a woman, the busy level of lavatory stalls at stationsincreases. Thus, the prediction algorithm executed by the server 104 mayidentify whether each user drunk coffee before leaving for work topredict the likelihood that the user uses a lavatory. The user, whochecks the busy-level fluctuation information 504 reflected a result ofthe prediction on the smartphone 501 can be more accurately aware of anavailable lavatory. It should be noted that the server 104 collectsgenders of the users as the attribute information from the informationinput terminals 101 d and 102 d and conducts prediction based on this,thereby presenting to the users the busy-level fluctuation informationwhich is generated distinguishing between busy statuses of men's andwomen's lavatories. It should be noted that the attribute informationmay not include gender and the server 104 may obtain locations of men'slavatories and women's lavatories at stations to identify whether eachuser used any of the lavatories in the past from the historical locationinformation of the user and estimate the gender of the user.

It should be noted that in the specific examples of the presentembodiment (the examples where the home appliances are toilets andothers), the mobile 101 b, the information input terminal 101 d, theinformation presentation device 105, and the server 104 shown in FIG. 2may be integrated as the mobile 101 e (see FIG. 10).

The examples of the information providing system, which provides theinformation on the prediction of a busy status of a lavatory at astation and the like, have been described, wherein the home appliances101 a and 102 a are toilets in the houses. A lavatory in a house and alavatory at a station, for example, are means for accomplishing asimilar purpose. Thus, the prediction is based on a given connectionbetween the lavatory usage in a house and the demand or the busy statusof a lavatory at a station, for example, and is thus effective.

Embodiment 4

Hereinafter, refrigerators by way of example of the home appliances 101a and 102 a in the above-described information providing system 100 willbe mainly described, with reference to FIGS. 1 and 2.

The information providing system according to the present embodimentestimates a meal time for each user.

In the information providing system according to the present embodiment,home appliances 101 a and 102 a which are refrigerators, each havecommunication functionalities and include means (a detection mechanism)for detecting use of the refrigerator by a user (individual). Thedetection mechanism includes open/close sensors of a refrigerator door,and every time the user opens the refrigerator door the refrigeratortransmits to the server 104 lifestyle information indicating that therefrigerator is used (e.g., time at which the door is opened), and arefrigerator ID (the home appliance ID) for identifying the refrigeratorvia the network 103. It should be noted that for a refrigerator having aplurality of doors, the refrigerator also sends to the server 104information for identifying a cabinet (such as a vegetable cabinet, arefrigerator cabinet, a freezer cabinet, and an ice maker cabinet) thatis opened by a door in the lifestyle information.

The server 104 can collect refrigerator usage history (e.g., informationon a cabinet used at each time of use) for each user. The use historyallows the server 104 to estimate a meal time slot of the user topredict user behavior, such as the next meal time, and provide theinformation presentation device 105 with provision information which isgenerated based on a result of the prediction and the locationinformation of the user. The provision information is presented by theinformation presentation device 105 to a movie theater operatingcompany, for example. Having seen the provision information, the movietheater operating company can know plans for meals of users, who came toa movie theater, after watching movies. For example, if a largepercentage of the users is predicted to be hungry due to a fact that along time has elapsed since an average meal time of the users who cameto the movie theater, the server 104 can provide, before movies areshown, advertisements related to meals, an information presentationdevice 105 installed at the theater broadcasts the advertisementsrelated to meals. This can broadcast the advertisements effectively.

Moreover, if the server 104 generates, as the provision information,information indicating the percentage of hungry users after watchingmovies, the movie theater operating company or the like having obtainedthis information via the information presentation device 105 caneffectively provide services, such as distributing discounted tickets toneighboring restaurants, when the percentage of hungry users is high.

It should be noted that the prediction algorithm executed by the server104 may predict the next meal time of each user, distinguishing betweena vegetable cabinet, a freezer cabinet, an ice maker cabinet, forexample, based on the lifestyle information which indicates a time atwhich each cabinet of the refrigerator is opened. This can determine aprediction algorithm that can more accurately estimate a meal time ofeach user, thereby more accurately predicting the next meal time.

Moreover, the home appliances 101 a and 102 a may not be refrigeratorsbut devices each having communication functionalities, such as alighting fixture, an induction cooktop, and a microwave oven at adinning room or a kitchen. Since intended uses of these devices areclosely related to meals, as with the case where the home appliances 101a and 102 a are refrigerators, the meal time of the user can beestimated also based on lifestyle information which indicates the usage(e.g., time of use) of the devices. It should be noted that the homeappliances 101 a and 102 a may each include a plurality of devices, suchas the refrigerator, a lighting fixture, an induction cooktop, and amicrowave oven at a dinning room or a kitchen. This allows more accurateestimation of a meal time of the user.

Moreover, the wearable sensors 101 c and 102 c may be glucose meters.This allows the server 104 to collect biometric information whichindicates a blood glucose level for each a user, allowing the server 104to estimate whether the user is hungry from historical blood glucoselevel, thereby more accurately predicting the next meal time of theuser.

Moreover, the information presentation device 105 may be a mobileterminal, such as a smartphone or a tablet, for providing the user withinformation. This allows the server 104 to provide pinpointadvertisements, such as a recommended restaurant, to a user owing themobile terminal which is the information presentation device 105. Itshould be noted that the server 104 may provide each user with anadvertisement of a restaurant close to the current location of the user,for example. Moreover, the server 104 may predict, based on thehistorical location information of the user, a path of travel of theuser in the near future from the current location, and provide anadvertisement of a restaurant close to the predicted path. In theadvertisement, information, such as the restaurant name, meal menu, thelocation of the restaurant, discount, and a privileged service, or usercondition (see the condition 301 of FIG. 12) which is estimated based onthe lifestyle information or the biometric information may be displayed.

Moreover, the wearable sensors 101 c and 102 c may be devices that havefunctionalities of measuring various component concentrations in blood,such as neutral fats and cholesterol, and transmit to the server 104 themeasured various component concentrations in blood as biometricinformation. Since the server 104 can estimate a health status for eachuser based on the obtained biometric information, the server 104 maygenerate provision information proposing a meal which is effective forimproving the health of the user and distribute the provisioninformation to the mobile terminal of the user as the informationpresentation device 105. The user condition (see the condition 301 ofFIG. 12) displayed on the mobile terminal may include information, suchas the various component concentrations in blood, and the possibilitythat the user fits to hyperlipemia, hypercholesterolernia, or diabetes,for example. This allows the user to select a meal that is effective forimproving the health of the user.

Moreover, the users may provide the information input terminals 101 dand 102 d with input of user's chronic health problems or constitutions,for example, and the input may be transmitted to the server 104 in theattribute information. This allows the server 104 to generate andprovide the users with provision information suited to improved healthof each user, based on the user's chronic health problem orconstitution. Moreover, in response to the input from the users, theinformation input terminals 101 d and 102 d may transmit to the server104 attribute information including information, such as user's favoritefood, whether the user is on a diet, and whether the user is a smoker.This allows the server 104 to generate provision information suited toeach user.

It should be noted that in the specific examples of the presentembodiment (the examples where the home appliances are refrigerators andothers) also, the mobile 101 b, the information input terminal 101 d,the information presentation device 105, and the server 104 shown inFIG. 2 may be integrated as the mobile 101 e (see FIG. 10).

Embodiment 5

In the following, a variation of the provision information generated bythe server 104 in the information providing system described in theembodiment 4 will be described. In other words, an example will bedescribed where the meal-related provision information which isgenerated, for each user, by the server 104 based on the lifestyleinformation, the biometric information, the location information, forexample, is provided in a rank format.

Here, the rank format is a format in which items (elements ofinformation) in the provision information are ordered based on a givenrating parameter (numeric value), and sequenced in order. Moreover, theordering is referred to as ranking.

For example, when the users have meal at restaurants, the wearablesensors 101 c and 102 c may measure biometric information for each user,such as the heart rate, before and after the meal, and the server 104may collect the information via the network 103. In doing so, the serverestimates a degree of parasympathetic nerve activities when each user ishaving a meal, from the heart rate, for example, thereby rating effectsof restaurants and meal menu for stimulating parasympathetic nerves.Thus, the provision information generating unit 92 of the server 104 maygenerate provision information which represents restaurants and mealmenu in a rank format, according to a numeric value indicating a resultof the rating. The provision information is transmitted from the server104 to an information presentation device 105, which is a smartphone orthe like of each user, for example, and the information presentationdevice 105 displays the provision information on a screen, for example.

FIG. 14 shows an example display displaying restaurants in a rank formaton a smartphone 701.

Each user who sees the screen shown in FIG. 14 can search a restaurantsuited to user's preference or for improved health. In recent years,restaurant ranking by individual's subjective rating, such as so-calledword of mouth, is widely known. However, ranking that is based on thebiometric information as shown in the present embodiment has lesspotential of manipulation of information, and thus more reliable.

In the following, the example display shown in FIG. 14 will bedescribed.

On the screen of the smartphone 701, included are: a title 702, noted as“Ranking of restaurants serving food for enhancing immunity;” a subtitle703, noted as “Ranking of restaurants serving food for stimulatingparasympathetic nerves;” a category 704; a rank order 705; a restaurantname 706; a rating parameter 707; a restaurant location 708; and awaiting time 709.

Here, the title 702 is information indicative of meaning (effects) ofvalues of items indicated by the rating parameters (numeric values)which the ranking is based on.

The subtitle 703 is information indicating the meaning of the ratingparameters and a method of the rating. This makes content of the ratingeasier for the user to understand.

The category 704 classifies meals, such as “Western,” “Japanese,” and“Chinese.” This allows the user to select a category from which the userwants to have a meal and search a highly rated restaurant. Furthersubdivided categories may be used. Moreover, a plurality of hierarchicalgroups may be previously prepared, such as a large group category, amedium group category, and a small group category, and a user, who viewsthe information, may be allowed to select a hierarchical group accordingto need. For example, the large group category may include “Japanese,”“Western,” “Chinese,” “Italian,” and “Cafe.” A medium group category for“Japanese” may include “Noodles,” “Bowls,” “Sushi,” “Japanese teishoku(set meal),” and “Yakiniku (grilled meat),” and a small group categoryfor “Noodles” may include “Soba” and “Udon.”

The rank order 705 is descending order from a highest rating parameter(numeric value). This clearly indicates highly rated restaurants.

The restaurant location 708 is indicated by distance from the currentlocation. The restaurant location 708 and the current waiting time 709make it easy for the user to select a restaurant, considering a timebefore a meal.

Such information in the rank format is not limited to information suchas restaurants highly effective for helping stimulating parasympatheticnerves, and may include information on places and shops where householdswith infants frequent, or information on shops where hayfever sufferersfrequent or meal menu for the hayfever sufferers. For example, the homeappliances 101 a and 102 a may be bathroom ceiling heater fans, and theserver 104 may collect bathroom ceiling heater fan usage for each useras the lifestyle information. The prediction algorithm executed by theserver 104 may estimate a user, who is increasingly use the bathroomceiling heater fan in a particular season (e.g., around March) otherthan rainy season to be a hayfever sufferer, for example. Accordingly,the information generation algorithm executed by the server 104 mayextract and generate shops where hayfever sufferers frequent as theprovision information, based on the location information of shops andthe historical location information of the user who is estimated to be ahayfever sufferer. It should be noted that the information generationalgorithm may include a configuration to obtain weather information andestimate a user who uses the bathroom ceiling heater fan on a clear dayin the particular season (e.g., around March) to be a hayfever sufferer.

Moreover, a plurality of places (such as a park, a facility, and ashop,) may be ordered based on a result of, for example, rating based onthe number of users visited, who are estimated to be in particularcondition based on the lifestyle information or the like, and the placesmay be presented in a rank format. Moreover, information on each usercondition estimated based on the lifestyle information or the like maybe presented in a format distinguishing on a condition-by-conditionbasis.

Moreover, the information generation algorithm executed by the server104 may extract places where households with infants frequent, based onthe historical location information of a user for each householdestimated to have an infant, and generate the provision information fromthe extraction. It should be noted that the method shown in theembodiment 2, for example, may be used to estimate a household with aninfant.

Moreover, in the case where the lifestyle information which indicates,for example, how long lighting in a children's room is used can becollected from the home appliance, if the duration of use of lighting isincreased as compared to the previous year, it can be estimated that thehousehold is with a student who is an applicant preparing for anexamination. Moreover, in the case where the lifestyle information whichindicates duration of use of the home appliance in the household can becollected from the home appliance, if frequency of use of the homeappliance on daytime is less than nighttime, it can be estimated that auser of the home appliance is of a two-income couple. As such,characteristics of users may be extracted from the lifestyleinformation, and users having the same characteristics may be grouped,and restaurants or meal menu may be ranked as described above for eachgroup.

Moreover, the user characteristics obtained based on the lifestyleinformation, restaurants used by the users and are obtained based on thelocation information, and rating of restaurants obtained based on thebiometric information may be ranked together. Information better suitedto users can be provided by ranking for each category based on usercharacteristics, such as ranking of restaurants serving food forenhancing immunity of a household with an infant or ranking ofrestaurants serving food for enhancing immunity of hayfever sufferers,for example.

Moreover, while the example shown in FIG. 14 is the content that isgenerated based on the biometric information indicating the heart rate,the biometric information may be other than heart rate. For example, inthe case where the wearable sensors 101 c and 102 c are clinicalthermometers and the biometric information indicating body temperatureis used, ranking of restaurants serving food for warming body of theuser can be provided based on results of measurement of body temperaturebefore and after having food and drink.

Moreover, not limiting to the ranking of restaurants as described above,other places and facilities may be ranked as well. For example, heartrates before and after having food and drink or before and after havinga bath are measured, and surges of parasympathetic nerve activities or adegree of the user being relaxed is estimated, thereby allowing rankingrestaurants or spa which induce relaxing effects. Moreover, collectingthe location information of each user whose sympathetic nerve activitysurges allows ranking amusement parks (attractions) which provideexcitement to the user. As such, in the information providing system,collecting the biometric information of a group of users allows varietypieces of information to be provided in a rank format. It should benoted that the rank formats described herein are applicable as a formatwhereby the information in each embodiment is presented.

Embodiment 6

Hereinafter, a vehicle navigation device, mounted on the vehicle, by wayof example of the mobiles 101 b and 102 b and the informationpresentation device 105 in the information providing system 100 setforth above will be mainly described, with reference to FIGS. 1 and 2.

An information providing system according to the present embodimentproposes a route that is suited to a user driving a vehicle, inaccordance with lifestyle information which is obtained from a homeappliance based on the usage of the home appliance by the user. Itshould be noted that the proposed path is different depending on a user.

Herein, first, suppose that the home appliances 101 a and 102 a aretoilets in the houses as shown in the embodiment 3, and toilet usage(e.g., time of use, duration of use) is transmitted as the lifestyleinformation to the server 104.

The server 104 accumulates a toilet usage history for each user, forexample, estimates whether the user is in a state where the user wantsto use a lavatory outside household, based on a predetermined predictionalgorithm to predict likelihood that the user uses an external lavatory.Moreover, based on a predetermined information generation algorithm, theserver 104 generates provision information for proposing an appropriateroute of travel for vehicle navigation, in accordance with whether theuser is likely to use a lavatory, based on the location information ofthe user. The server 104 then transmits the provision information to theinformation presentation device 105 which is a vehicle navigation deviceowned by the user. In response, the information presentation device 105presents the route of travel.

It should be noted that regarding the processing (a travel routepresentation process), for proposing a route of travel, of estimatingand predicting the user condition and generating and presenting theprovision information, the configuration of partitioning the processingbetween the server 104 and the information presentation device 105 onthe system configuration can vary. The information presentation device105, which is the vehicle navigation device, may generate some of theprovision information. Alternatively, the server 104 may receiveinformation input from the user through the information presentationdevice 105, such as a destination, and generate provision informationindicating the route of travel. For example, as a system configuration,the mobile 101 b, the information input terminal 101 d, the informationpresentation device 105, and the server 104 may be integrated as themobile 101 e as shown in FIG. 10.

It should be noted that in the following, for purposes of description,description continues where the information providing system 100 has thesystem configuration as shown in FIG. 10 and the mobile 101 e is thevehicle navigation device mounted in the vehicle.

The mobile 101 e obtains map information including information, such aslocations of roads and locations of toilet provider facilities byexternally receiving them, for example. The mobile 101 e includesfunctions needed for vehicle navigation. The location information of thetoilet provider facilities are provided by, for example, the toiletprovider facilities or a service provider which provides map informationfor vehicle navigation.

FIG. 15 is a flowchart illustrating the travel route presentationprocess using the lifestyle information on use of toilet.

As illustrated in the figure, the mobile 101 e obtains toilet usagehistory information for each user in a household (step S1501), anddetermines whether duration (a time of stay) of immediately previous useof the toilet (before getting in a vehicle) is less than five minutes(step S1502). If the duration of use is less than five minutes, themobile 101 e presents a shortest route to a pre-set destination (stepS1503). If the duration of use is five minutes or longer, the mobile 101e presents a route (Hereinafter, referred to as “a multi-lavatoryroute.”) along which a greater number of public lavatories and toiletprovider facilities (e.g., convenience stores) is present (step S1504).The travel route presentation process is a process, assuming that ifduration of use of toilet is about five minutes or longer, it isestimated that the user has diarrhea. This allows proposal, to a userwho has diarrhea, of a multi-lavatory route where the user can drivewithout worry.

Moreover, the travel route presentation process may receive selection bythe user.

FIG. 16 is a flowchart illustrating a travel route presentation processincluding user selection.

As illustrated in the figure, the mobile 101 e obtains the toilet usagehistory information of the user in the household (step S1501), anddetermines whether duration (a time of stay) of immediately previous useof the toilet (before getting in a vehicle) is less than five minutes(step S1502). If the duration of use is less than five minutes, themobile 101 e presents a shortest route to a pre-set destination (stepS1503). If the duration of use is five minutes or longer in step S1502,the mobile 101 e receives selection input from the user as to whetherthe user wishes for presentation of a multi-lavatory route (step S1601),and presets a multi-lavatory route only if the multi-lavatory route isselected (step S1504). This allows proposal of a multi-lavatory route toa user who is really in need for it.

In the following, a user interface (screen) of the vehicle navigationdevice when the multi-lavatory route is presented to the user in stepS1504 of FIG. 16 will be described. It should be noted that the vehiclenavigation device includes a touch panel, allowing the user to makeselection by touching a button displayed on the screen, for example.

FIG. 17 shows the screen of the vehicle navigation device where themulti-lavatory route is selectively displayed, in addition to a map.FIG. 18 shows the screen of the vehicle navigation device where ashortest route is selectively displayed, in addition to the map.

Route selection buttons 1301 shown in FIGS. 17 and 18 are for allowingthe user to select one of a shortest route and a multi-lavatory route.The user can make selection by touching a region where a button isdisplayed. The currently displayed one of the shortest route or themulti-lavatory route is highlighted (displayed in reverse type). Inother words, the multi-lavatory route is selectively displayed in FIG.17, and the shortest route is selectively displayed in FIG. 18.

In the figure, a mark 1302 represents the current location of the useron a map, and a route 1303 indicates a route of travel by a bold line. Alavatory sign 1304 represents a location of a toilet provider facilityon a map, and an in-use mark 1305 represents that a lavatory indicatedthereby is in use.

As illustrated in FIG. 17, a lavatory signs 1304 are displayed at thelocations of toilet provider facilities on the route of travel for auser who has chosen multi-lavatory route. This allows the user to bereadily aware of the location of a lavatory.

The in-use mark 1305 is displayed on the side of a lavatory sign 1304indicating that all lavatories (stalls) installed at the toilet providerfacility are unavailable. This allows the user to select a lavatorypredicted to be available in a short time.

Moreover, although not shown, the number of lavatories (stalls)installed at each toilet provider facility may be displayed next to thelavatory sign 1304. This allows the user to estimate an availablelavatory in a shorter time.

Moreover, while the foregoing example gives the alternative between ashortest route and a multi-lavatory route, three or more routes oftravel may be presented, such as “Fastest route,” “Shortest route,”“Energy-saving route,” “Street-only route,” and “Multi-lavatory route,”as shown in FIG. 19. The user can select a desired route of travel,among a large number of routes of travel, by touching around the textarea of a route name of the desired route.

Moreover, as illustrated in FIG. 19, expected time to arrive at thedestination for each route of travel, if selected, may be displayed.This allows the user to be aware of the advantages and disadvantages ofselecting each route of travel, thereby accurately selecting a desiredroute of travel. Moreover, as illustrated in FIG. 19, an expected amountof fuel consumed for each route of travel, if selected, to arrive at thedestination may be displayed. This also allows the user to be aware ofthe advantages and disadvantages for each route of travel, therebyaccurately selecting a desired route of travel. It should be noted thatin the example display of FIG. 19, liter is used as a unit for fuel tocorrespond to gasoline-powered vehicles, hybrid-powered vehicles, or thelike. However, electric energy unit, such as “Wh”, may be used tocorrespond to electric vehicles, fuel-cell vehicles, or the like.Alternatively, required electricity rate price may be used as the unit.In the case where the unit is displayed in price, if the route of travelincludes a toll road, a toll for the toll road may be added together anddisplayed.

Moreover, on the screen of the vehicle navigation device, informationmay also be displayed as to whether refueling, such as charging orgasoline refueling, is necessary before the destination, based onavailable fuel, such as battery power or gasoline level. This allows theuser to compare the advantages and disadvantages of the routes oftravel, if selected, thereby accurately selecting a desired route oftravel.

In the above example, whether the user has diarrhea is estimated basedon the lifestyle information which is toilet usage history. Other thanthis, a toilet may include means for measuring a state of body waste,the measured state may be included in the lifestyle information, andwhether the user has diarrhea may be estimated based on the lifestyleinformation. For example, the toilet may take a picture of body wasteand the generated image may be included in the lifestyle information.The image may be then analyzed to estimate whether the user hasdiarrhea. This allows more accurate estimation of the user condition andpresentation of a best suited route of travel to the user.

In the foregoing example, whether a multi-lavatory route is to beproposed to the user is determined based on the lifestyle information,such as the duration of use of a toilet, wherein five minutes is used asa baseline with respect to the duration of immediately previous use ofthe toilet. The criteria, however, may be another one (another baselineobtained from the lifestyle information or the biometric information, ora baseline obtained from another lifestyle information). Alternatively,the user may be allowed to specify an algorithm for the determination.

Next, the configuration of the information providing system will bedescribed in which the home appliances 101 a and 102 a are lightingfixtures in houses, and lighting usage (e.g., time of use, duration ofuse) is collected for each user as the lifestyle information, and aroute of travel suited to the user is proposed based on the lifestyleinformation.

FIG. 20 is a flowchart illustrating the travel route presentationprocess utilizing lifestyle information on use of lighting.

As illustrated in the figure, the mobile 101 e obtains lighting usagehistory information of the user in the household (step S2001), anddetermines whether duration of lighting of all rooms being turned OFF isfive hours or longer in the previous night (step S2002). If it is fivehours or longer, the mobile 101 e presents a shortest route to a pre-setdestination (step S2003). If it is less than five hours, the mobile 101e presents a route (Hereinafter, referred to as the “multi-stimulusroute.”) avoiding routes where the user is likely to fall asleep, suchas a highway where no road light is present (step S2004). The travelroute presentation process is a process, assuming that if the durationof the lighting of all rooms being turned OFF in the previous night isfive hours or longer, it is estimated that the user is short of sleep.This allows proposal, to a user who has strong sleepiness due to lack ofsleep, of a multi-stimulus route where the user can drive without worry.

Moreover, for presentation of the multi-stimulus route also, similarlyto the presentation of a multi-lavatory route described above, themobile 101 e may receive selection made by the user to present themulti-stimulus route only when desired by the user. For presentation ofthe multi-stimulus route also, the expected time to arrive at thedestination may be displayed.

Moreover, the wearable sensor 101 c may be a device configured with anelectrode mounted on a steering wheel of the vehicle so as to be incontact with the driver's hand, and measure the heart rate of the drivervia the hand. Frequency analysis can be performed on heart rateintervals, based on biometric information which indicates the heartrate. If fluctuation in the interval is simple and low, it can beestimated that the driver is sleepy. Thus, combined use of thisestimation and the estimation that the user is short of sleep based onthe above lifestyle information allows more accurate determination thatthe driver is very sleepy.

Moreover, the home appliances 101 a and 102 a may each include one ormore meal-related devices which transmit the home appliance usage byeach user (a time of use and so on) as the lifestyle information.Examples of the meal-related devices include a cooking home appliance(an induction cooktop, a refrigerator, or a microwave oven), adishwasher, and a lighting fixture at a dinning room or a kitchen.

In this case, whether the user had a meal before going out (beforegetting in the vehicle) can be estimated based on the lifestyleinformation, and the longer the time from when the user had the meal tothe user gets in the vehicle (e.g., if the user had no meal in theprevious five hours), the user is predicted to be more likely to stop byat a restaurant. Thus, a route in which a large number of mealproviders, such as restaurants, are at the roadside (Hereinafter,referred to as “multi-restaurant route.”) may be presented. Moreover,the mobile 101 e may receive selection made by the user to present amulti-restaurant route only when the user desires.

In each example described above, various routes of travel, such as amulti-lavatory route and a multi-stimulus route, are proposed to eachuser, based on the lifestyle information of the user. Thus, rather thanpresenting all routes of travel to all users, a route of travel isproposed for each user focusing on one that is predicted to be necessaryfor the user, thereby alleviating the burden (labor) for the user ofselecting a desired route of travel.

In the following, description is given where the information providingsystem has the system configuration as shown in FIG. 2, and the mobiles101 b and 102 b and the information presentation device 105 are includedin a vehicle navigation device mounted in a vehicle.

The wearable sensors 101 c and 102 c may transmit biometric information,such as the user's heart rate, to the server 104, and the mobiles 101 band 102 b may be vehicle navigation devices which are each mounted in auser's vehicle and transmit the location information to the server 104.In this case, the server 104 collects, for each user (driver),sleepiness based on a heart rate and the location information, forexample, thereby knowing a road (location) on a map where the driver islikely to feel sleepiness. This can generate information on roads wherea large number of vehicle drivers felt sleepiness on a map. Using this,a multi-stimulus route can be proposed to a user who is short of sleepor a user feeling sleepiness.

It should be noted that a degree of stimulation may be previouslycalculated for each road and, using this, a multi-stimulus route may beproposed. In the following, an example of a method for calculating thedegree of stimulation will be shown.

For example, a reciprocal of a mean value of levels of sleepiness of aplurality of drivers may be used as the degree of stimulation. The levelof sleepiness may be proportional to low fluctuation in heart rate.Moreover, to calculate the degree of stimulation, not only the biometricinformation on the heart rate but also the lifestyle information, suchas lighting usage history, may be used together. For example, the degreeof stimulation may be weighted more with a decreased length of hoursslept the previous night. For example, the previous night's sleep timeof each driver may be standardized, where a mean value of the previousnight's sleep time of all the drivers is 1. Then, a mean value obtainedby dividing, for each driver, the level of sleepiness by the previousnight's sleep time (after standardization) may be calculated for eachroad, as the degree of stimulation. Weighting the degree of stimulationby the previous night's sleep time as such allows a driver who had shortsleep time in the previous night to be aware of stimulating roads. Thus,more appropriate road can be selected to be proposed as a multi-stimulusroute to the driver who had short sleep time.

Moreover, as another example using the information on the previousnight's sleep time, a mean value of levels of sleepiness, limited todrivers whose previous night's sleep time is less than five hours, maybe calculated for each road. This also allows more appropriate selectionof roads to be proposed as a multi-stimulus route to the driver who hadshort sleep time.

The above description provides the example where the information ispresented to the user, using the lifestyle information and the locationinformation. In addition to the example where the information ispresented to the user, a system configuration is also possible where,for example, the use history of a toilet or lighting, or the lifestyleinformation, such as the information on body waste, is used to supplydata (information) for controlling the devices (e.g., a device includedin a mobile owned by the user).

For example, the estimation may be made that the user is short of sleepbased on duration of the lighting of all rooms being turned OFF and anair conditioner mounted in the vehicle may perform control to conditionair so that concentration of carbon dioxide in the vehicle decreases.This control is achieved by providing the air conditioner with controlinformation. The concentration of carbon dioxide is measured by an airconditioner internally or externally including a concentration meter ofcarbon dioxide using infrared spectroscopy. Moreover, if the duration ofthe lighting in all rooms being turned OFF in the previous night is fivehours or longer, air exchange may not be conducted between outside andinside the vehicle, and if the duration is less than five hours the airexchange may be conducted outside and inside the vehicle. This serves toreduce sleepiness of the user who is short of sleep, and also serves,for a user without sleep deprivation, to reduce power consumed by theair conditioner. Output of means (home appliance) for measuring theduration of use of a toilet or a state of body waste (components) may beused as the lifestyle information to estimate whether the user hasdiarrhea, and adjust air-conditioned temperature inside the vehicle inresponse to a result of the estimation. The air is conditioned, such asraising the set temperature for the user who has diarrhea and loweringthe set temperature for the user who does not have diarrhea, thereby airconditioning in accordance with the user condition.

Moreover, it may be estimated that the user is short of sleep from thetime at which the lighting of all the room is turned OFF, and the degreeof braking of the vehicle may be adjusted. Heavy braking may be providedin response to depression of the brake for a driver who has insufficientsleep. This can prevent the driver, who is short of sleep and thus hashigh probability of causing an accident, from causing an accident, andprovide a driver, who has sufficient sleep, with driving with less hardbreaking and reduced load that is due to a sudden stop.

The example has been described where the biometric information on theheart rate is used to estimate sleepiness of the user (the driver).However, based on the similar biometric information, it may also beestimated that the user is in a state where an incident has happened,from changes in heart rate. Here, an incident refers to an incident thatdoes not lead to, but may well have resulted in, a severe disaster or anaccident. If there is a rapid rise in heart rate, it may be estimatedthat an incident has happened. The estimation of an incident is alsopossible based on other than the heart rate. For example, skinconductance measured by the wearable sensor, which can measure skinconductance, may be collected as the biometric information, and if thereis a rapid rise in skin conductance, it may be estimated that anincident has happened. Moreover, happening of an incident may be moreaccurately estimated by making the estimation using both skinconductance and heart rate information. Then, a place where the incidenthas happened can be identified from the location information of the user(the driver). Thus, a plurality of pieces of information from aplurality of users are collected and integrated, thereby identifyingplaces (roads) where the drivers are likely to feel frightened, andproviding the user or the like with information (Hereinafter, referredto as “incident information.”) on (high risk) roads where an accident islikely to happen.

FIG. 21 shows the screen of the vehicle navigation device where incidentinformation is displayed, in addition to a map.

Incident information selection buttons 1601 shown in FIG. 21 allow theuser to select either an option of displaying only incident informationrelated to a driver who is short of sleep or an option of displayingincident information related to all drivers. The user can make theselection by touching a region where a button is displayed. Thecurrently selected one of the option of displaying only the incidentinformation related to a driver who is short of sleep or the option ofdisplaying the incident information related to all drivers ishighlighted (displayed in reverse type).

In the figure, a mark 1302 represents the current location of the userand the double line 1602 represents a road on a map. The bold line 1603indicates a road where an accident is likely to happen. To distinguishwhether the road where an accident is likely to happen is a forward pathor a return path, the bold line 1603 is attached to a correspondingforward path or return path. Also for example, a percentage of driverswho have experienced an incident may be represented by a thickness ofthe bold line 1603. An attention mark 1604 is displayed on anintersection where an accident is particularly highly likely to happen.It should be noted that accident information may be collected to displaythe attention mark 1604 on a place where an accident has actuallyhappened. The attention mark 1604 allows the user to be notified of aplace where the user should pay particular attention.

Moreover, instead of the display of the incident information asillustrated in FIG. 21, information may be displayed with respect toplaces at which a large number of drivers fall in a state where theabove-described level of sleepiness is high. Also for example, awaveform of a heart rate of a driver who stopped a car at a spa facilityfor 30 minutes or longer is measured, the surges of parasympatheticnerve activities are calculated from changes in waveform of the heartrate before and after using the spa facility and rated as a degree ofthe driver being relaxed, thereby identifying spa facilities for thedriver to relax. Thus, spa facilities for drivers to relax can also bedisplayed on a map. Likewise, restaurants for drivers to relax can alsobe identified and displayed on the map.

Moreover, a microphone may be installed in the vehicle and the user maybe measured by the number of times the user sneezes after getting in thevehicle and the measurement result may be used together with thelocation information of the user. Thus, a degree of air pollution isestimated from the number of times the user sneezes, thereby displayinginformation on the degree of air pollution on a map. In this case, ifthe information as to whether the user has hayfever is obtained, theinformation for different causes of air pollution can be separatelyprovided on the map.

Moreover, the vehicle navigation device which has the function ofobtaining the location information for each user and the function ofpresenting the information to the user as described above may be amobile terminal, such as a smartphone or a tablet. This allows the userowning the mobile terminal to know appropriate route to travel (theroute of travel). Moreover, the vehicle navigation device may have thefunction of presenting the information to the user and the mobileterminal may have the function of obtaining the location information ofthe user.

Embodiment 7

In the following, a wearable sensor 1701 by way of example of thewearable sensors 101 c and 102 c set forth above will be described.

FIG. 22 is a diagram showing an example of an in-ear wearable sensor.

The wearable sensor 1701 is worn inserted into the ear. The wearablesensor 1701 may include at least means for measuring a sound in the ear.Various sounds can be measured in the ear. The in-ear wearable sensor1701 measures one or more of heart sounds, breathing sounds, and voice,thereby estimating emotional condition of the user. It should be notedthat a heart rate is derived from heart sounds, and a respiratory rateis derived from breathing sounds, which can be used to estimate user'semotion. Moreover, measuring the voice allows user conversation to berecorded. Furthermore, voice of a person whom the user has conversationwith may also be measured. This allows the one whom the user hasconversation with to be recorded as well.

Moreover, to highly precisely measure the voice of the one whom the userhas conversation with, the in-ear wearable sensor 1701 may be of anunsealed type, rather than a sealed type which seals passage of air intoand out of the ear.

Moreover, instead of including, therein, communication means forexternally transmitting the biometric information, the wearable sensor1701 may be connected to an information processing device 1702 whichincludes communication means. This can reduce the size and weight of aninsert which is inserted into the ear. This also alleviates the burden,to the user, of wearing the wearable sensor 1701. It should be notedthat information processing device 1702 may be the mobile 101 b or 102 bwhich detects the location information, and may further include thefunctionalities of the information presentation device 105.

SUPPLEMENTARY

While the embodiments of the information providing system whichimplements the information providing method have been described above,it is to be understood that the above embodiments are merelyillustrative and various modifications can be made.

For example, the mobiles described above only need to be smartphones andvehicles that can be owned and can travel with the user. The concept ofthe mobile includes a device which is inside an airplane and connectableto a network, such as a device installed at each seat in the airplane,and a device included in a bus which is connectable to a network, suchas an air-conditioning system in the bus. Thus, for example, theinformation providing system may be configured to achieve supplyingcontrol information to an air conditioner or the like to air conditionalong with the condition of the user who is short of sleep on anairplane or a bus, using the lifestyle information of the user.Moreover, the information providing system may be configured to achieveproviding information that is suited to the user on an airplane or abus, for example.

Part or the whole of the various processes (e.g., the processesillustrated in FIGS. 3 to 7, 11, 15, 16, and 20) performed by thedevices described above may be performed by mechanisms (hardware) of thedevices or executed by software. The execution of the process bysoftware is conducted by a processor included in a device executing thecontrol program stored in the memory. The control program may be storedin a recording medium and distributed. For example, the distributedcontrol program is installed into the device and the processor of thedevice is caused to execute the program, thereby causing the device toperform the process (such as the processes illustrated in FIGS. 3 to 7,11, 15, 16, and 20).

Moreover, a server 200 as an aspect of the present invention, as shownin FIG. 23, includes: a lifestyle information obtaining unit 210 whichobtains lifestyle information by receiving, from each of plural devicesused by plural users, information on a state of operation of the device;a location information obtaining unit 220 which obtains locationinformation of each user traveled, by receiving the locationinformation; and an information generation unit 230 which forecasts,using a processor, demand for a particular item or a particular serviceat a particular place, based on the lifestyle information obtained bythe lifestyle information obtaining unit 210 and the locationinformation obtained by the location information obtaining unit 220, andgenerate information to be provided, based on a result of forecastingthe demand.

Moreover, a mobile 300 as an aspect of the present invention is, asshown in FIG. 24, a mobile which provides a user who uses a device withinformation, the mobile including: a lifestyle information obtainingunit 310 which obtains lifestyle information, which is information on astate of the device used by the user, by receiving the lifestyleinformation; a location information obtaining unit 320 which obtainslocation information of a user by detecting a location to which the usertraveled; an information generation unit 330 which generates informationto be provided to the user, in accordance with the lifestyle informationand the location information; and an information presentation unit 340which presents to the user the information generated by the informationgeneration unit.

In other instances, various modifications to the above-describedembodiments that may be conceived by those skilled in the art orembodiments implemented by any combination of the components andfunctions shown in each embodiment are also included within the scope ofthe present invention.

INDUSTRIAL APPLICABILITY

The present invention is useful, for example, as a server or a mobile toprovide beneficial information that is not necessarily correlated to thesituation of the vital functions of each user.

REFERENCE SIGNS LIST

-   11 Lifestyle information recording unit-   12, 22, 32, 42, 52, 61, 61 e Communication unit-   21 Biometric information measurement unit-   31, 31 e Input receiving unit-   41, 41 e Location measuring unit-   51, 51 e Information presentation unit-   71, 71 e, 210, 310 Lifestyle information obtaining unit-   72, 72 e Biometric information obtaining unit-   73, 73 e Attribute information obtaining unit-   74, 74 e Registration information obtaining unit-   75, 75 e, 220, 320 Location information obtaining unit-   81, 81 e Lifestyle information storage unit-   82, 82 e Biometric information storage unit-   83, 83 e Attribute information storage unit-   84, 84 e Registration information storage unit-   85, 85 e Location information storage unit-   91, 91 e Behavior prediction unit-   92, 92 e Provision information generating unit-   100 Information providing system-   101, 102 House-   101 a, 102 a Home appliance-   101 b, 102 b, 101 e, 300 Mobile-   101 c, 102 c, 1701 Wearable sensor-   101 d, 102 d Information input terminal-   103 Network-   104, 200 Server-   105 Information presentation device-   230, 330 Information generation unit-   340 Information presentation unit

1. An information providing method for providing a user who uses adevice with information via a mobile owned by the user, comprising: (a)estimating a habit, which is a pattern of behavior of the user in aspecific time slot, from lifestyle information obtained by a lifestyleinformation obtaining unit receiving the lifestyle information from thedevice used by the user, the lifestyle information being information ona state of operation of the device, and if behavior of the user in thespecific time slot, which is most recent, does not match the estimatedhabit, predicting that the user is to act on behavior corresponding tothe estimated habit after the specific time slot; and (b) generating theinformation to be provided to the user in accordance with locationinformation indicating a location to which the user traveled andobtained by a location information obtaining unit receiving the locationinformation, the information being information on the behavior on whichthe user is predicted to act.
 2. The information providing methodaccording to claim 1, wherein the location information obtaining unitsuccessively obtains the location information of the user, and in step(a), behavior of the user at a location derived from a plurality ofpieces of the location information on the user obtained by the locationinformation obtaining unit is predicted from the lifestyle informationobtained by the lifestyle information obtaining unit.
 3. The informationproviding method according to claim 2, wherein in step (a), userbehavior related to an item or service that has a given connection withan intended use of the device is predicted, and in step (b), informationrelated to the item or the service is generated.
 4. The informationproviding method according to claim 3, wherein the lifestyle informationobtaining unit obtains a plurality of pieces of lifestyle information byreceiving the plurality of pieces of lifestyle information from pluraldevices used by plural users, and step (a) is predicting behaviors ofthe plural users at locations derived from a plurality of pieces oflocation information of the plural users from the plurality of pieces oflifestyle information obtained from the plural devices used by theplural users.
 5. The information providing method according to claim 4,further comprising (c) attempting, for each of the plural users, toobtain consent from the user to obtain lifestyle information from adevice among the plural devices which is used by the user before thelifestyle information obtaining unit obtains the lifestyle information.6. The information providing method according to claim 5, wherein instep (b), the information to be provided is generated in a mannerdistinguishing between the plural users to whom the information is to beprovided, so that the information provided to a user from which theconsent is obtained in step (c), among the plural users, includes morecontent than the information provided to a user from which the consentis not obtained in step (c).
 7. The information providing methodaccording to claim 2, wherein in step (a), the behavior of the user ispredicted based also on biometric information on the user obtained by abiometric information obtaining unit receiving the biometric informationfrom a device which measures the user.
 8. The information providingmethod according to claim 2, wherein the mobile detects and transmits alocation of the mobile, and the location information obtaining unitobtains the location information indicating the location of the user byreceiving the location information from the mobile.
 9. The informationproviding method according to claim 1, wherein the location informationobtaining unit successively obtains the location information, andsuccessively performs steps (a) and (b).
 10. An information providingmethod for providing control information to a device included in amobile owned by a user who uses a device, comprising: estimating ahabit, which is a pattern of behavior of the user in a specific timeslot, from lifestyle information obtained by a lifestyle informationobtaining unit receiving the lifestyle information from the device usedby the user who owns the mobile, the lifestyle information beinginformation on a state of operation of the device used by the user, andif behavior of the user in the specific time slot, which is most recent,does not match the estimated habit, predicting that the user is to acton behavior corresponding to the estimated habit after the specific timeslot; and generating the control information to be provided to thedevice included in the mobile in accordance with location informationindicating a location to which the user traveled and obtained by alocation information obtaining unit receiving the location information,the control information being information on the behavior on which theuser is predicted to act.
 11. A mobile which provides a user who uses adevice with information, the mobile comprising: a lifestyle informationobtaining unit configured to obtain lifestyle information by receivingthe lifestyle information, the lifestyle information being informationon a state of operation of the device used by the user; a locationinformation obtaining unit configured to obtain location information onthe user by detecting a location to which the user traveled; aninformation generation unit configured to estimate a habit, which is apattern of behavior of the user in a specific time slot, from thelifestyle information and if behavior of the user in the specific timeslot, which is most recent, does not match the estimated habit, predictthat the user is to act on behavior corresponding to the estimated habitafter the specific time slot, and generate the information to beprovided to the user according to the lifestyle information and thelocation information, the information being information on the behavioron which the user is predicted to act; and an information presentationunit configured to present the information generated by the informationgeneration unit to the user.